CHAPTER 1

INTRODUCTION

1.1. Client Identification/Need Identification/Identification of relevant

Contemporary issue

Client Identification:
Our Web-Based Smart Inventory Management System is designed for businesses of all
sizes, including retail stores, warehouses, manufacturers, and e-commerce platforms.
The system is also beneficial for logistics companies and supply chain managers seeking
better stock control and operational efficiency. Identifying our target audience helps us tailor
the system’s features to meet their specific inventory needs.
Need Identification:
i. Consumer Protection: Inventory Optimization: Many businesses struggle with
inefficient stock management, leading to overstocking, shortages, and financial losses.
Our system provides real-time tracking and automated updates to prevent these issues.

ii. Operational Efficiency: Manual inventory tracking is time-consuming and prone to

errors. Automating the process improves accuracy, reduces human effort, and
enhances productivity.

iii. Data-Driven Decision-Making: Businesses require analytics and reports to make

informed purchasing and stocking decisions. Our system provides valuable insights
into stock trends, sales performance, and demand forecasting.

iv. Multi-User Access & Security: Businesses often require role-based access control to
manage different levels of inventory operations securely.

v. Seamless Integration: Many businesses rely on ERP and POS systems. Our solution
ensures smooth integration with these existing platforms for a unified workflow.

Identification of Relevant Contemporary Issue:

i. Ethical Design in Technology: Supply Chain Disruptions: Recent global supply chain
challenges highlight the need for better inventory control and forecasting to minimize
disruptions.

ii. E-Commerce Growth: With the rapid expansion of online retail, businesses need smarter,

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 automated inventory solutions to keep up with demand.

iii. Sustainability in Inventory Management: Overproduction and waste are significant

concerns. Efficient inventory tracking helps reduce excess stock and supports sustainable
business practices.

iv. Data Security & Compliance: With increasing concerns over data protection, our system
ensures secure inventory management by complying with industry regulations and
implementing robust security measures.Identification of Problem

Understanding the core problems faced in inventory management is crucial to developing a

solution that addresses real-world challenges.
1. Inefficient Inventory Tracking
Issue: Many businesses rely on outdated manual tracking methods, leading to errors, stock
mismanagement, and operational delays.
Impact: Businesses suffer from lost sales due to stock shortages, excess costs from overstocking, and
delays in order fulfillment.

2. Lack of Real-Time Inventory Updates

Issue: Without real-time updates, businesses struggle with inaccurate stock levels.
Impact: Decision-making becomes difficult, leading to mismanaged supply chains and potential
financial losses.

3. Poor Demand Forecasting

Issue: Many businesses lack predictive analytics to forecast stock requirements accurately.
Impact: This results in overstocking (leading to waste and storage costs) or understocking (leading to
lost sales and dissatisfied customers).

4. Security and Data Management Concerns

Issue: Traditional inventory systems may lack proper access controls and security features, putting
inventory data at risk.
Impact: Unauthorized access can lead to data leaks, manipulation, or mismanagement of stock records.

5. Need for Integration with Other Business Systems

Issue: Many existing inventory solutions do not integrate well with ERP, POS, or accounting software.
Impact: Businesses face inefficiencies in data transfer, requiring manual input that increases errors and
wastes time.

By addressing these key challenges, our Web-Based Smart Inventory Management System aims
to streamline inventory control, improve accuracy, enhance decision-making, and provide a secure and
scalable solution for businesses.

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1.2. Identification of Tasks

To ensure a successful development process, we have outlined the key tasks involved in
building our Web-Based Smart Inventory Management System.
1. Research and Analysis
i. Business Requirements Analysis: Understand industry-specific needs for inventory
management.

ii. Technology Research: Identify the best tech stack for performance, scalability, and
security (e.g., React, Node.js, MySQL, and cloud-based storage).

iii. Compliance Study: Research data security laws and industry regulations related
to inventory management.

2. System Development
i. Feature Planning: Define core features like real-time stock updates, barcode/QR code
scanning, automated alerts, and reporting tools.

ii. User Interface Design: Create a modern, intuitive dashboard for easy navigation and
seamless stock management.

iii. Database Management: Develop a structured inventory database to efficiently store

and retrieve stock information.

3. Inventory Tracking & Automation

i. Stock Monitoring System: Implement real-time inventory tracking with automated
stock updates.

ii. AI-Driven Demand Forecasting: Use machine learning algorithms to predict stock
demand based on past trends and sales data.

iii. Automated Notifications: Develop a system that sends alerts for low stock levels,
reorder reminders, and expiry notifications.

4. Security and Data Privacy

i. Role-Based Access Control: Ensure secure access management for different users
(e.g., admin, manager, warehouse staff).

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 ii. Data Encryption & Security: Implement encryption protocols and authentication
mechanisms to protect sensitive inventory data.

iii. Regulatory Compliance: Ensure the system meets GDPR, CCPA, and other
industry security standards.

5. Integration with Other Business Tools

i. ERP & POS Integration: Enable seamless connectivity with existing enterprise
solutions.

ii. Cloud-Based Storage & Backup: Implement cloud storage for secure data backup and
remote access.

iii. API Development: Create APIs for third-party integrations and scalability.

6. Testing and Deployment

i. Unit & System Testing: Conduct rigorous testing to ensure functionality, security, and
performance.

ii. User Acceptance Testing (UAT): Get feedback from businesses and stakeholders
for final refinements.

iii. Deployment & Maintenance: Deploy the system on cloud servers and provide ongoing
updates and support.

By following this structured approach, our project ensures a robust, secure, and intelligent
v inventory management system that enhances efficiency, reduces operational costs, and n
v supports businesses in making informed decisions.

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1.3. Timeline

Fig 1.4.1 Gantt Chart

1.4. Organization of the Report

1. Title Page
 Displays the project title, "Web-Based Smart Inventory Management
System," along with the names of contributors, date, and affiliated institution.
2. Abstract
 Summarizes the project's objectives, methodology, and key features, providing a
high-level overview of how the system optimizes inventory management
through automation, real-time tracking, and data-driven decision-making.
3. Introduction
 Introduces the challenges of inventory management in businesses, the importance of
efficient stock control, and the motivation behind developing this smart inventory
system.
 Establishes the system’s objective to improve accuracy, reduce operational costs,
and enhance efficiency.
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4. Goals/Objectives
 Defines the specific objectives of the system, including:
 Real-time inventory tracking
 Automated stock management and alerts
 Seamless integration with ERP and POS systems
 Enhanced security and role-based access control
5. Literature Review
 Discusses existing inventory management solutions, their limitations, and how our
system addresses these gaps.
 Reviews research on automated stock tracking, cloud-based inventory solutions,
and AI-driven demand forecasting.
6. System Design and Features
 Provides an in-depth look at the system’s architecture and functionality, including:
 Database structure for efficient stock storage and retrieval
 User-friendly dashboard for intuitive navigation
 Barcode/QR code scanning for quick stock updates
 Predictive analytics for demand forecasting
 Automated low-stock notifications and reorder management
7. Design Constraints
 Highlights limitations encountered during development, such as:
 Balancing system complexity with ease of use
 Ensuring data security and regulatory compliance
 Handling scalability for businesses of different sizes
8. Implementation
 Details the development process, including:
 Technology stack selection (e.g., React, Node.js, MySQL, and cloud
storage)
 System deployment on cloud platforms
 Testing phases for functionality, performance, and security
9. Analysis of Features and Finalization Subject to Constraints
 Evaluates the system’s performance and effectiveness in reducing errors,
improving stock management, and enhancing decision-making.
 Discusses how constraints influenced feature development and prioritization.
10. Bibliometric Analysis

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 Reviews scholarly and industry research on inventory optimization, supply chain
efficiency, and digital inventory systems, analyzing trends and innovations in the field.
11. Future Directions
 Explores potential system enhancements, such as:
 AI-powered inventory forecasting
 IoT-enabled real-time stock tracking
 Mobile application for on-the-go inventory management
 Integration with blockchain for enhanced transparency
12. Conclusion
 Summarizes the system’s impact on streamlining inventory operations, reducing costs,
and improving stock accuracy.
 Reinforces the significance of automated inventory management in modern businesses.
13. References
 Lists all academic papers, articles, and resources referenced throughout the report.
14. Appendices (If Necessary)
 Provides supplementary materials such as system screenshots, sample reports, test results,
or user feedback to enhance understanding of the inventory management system

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 CHAPTER 2
LITERATURE REVIEW/BACKGROUND STUDY

2.1 Timeline of the reported problem

S.
No. Year Problem Identification Reference

[Industry Reports on
Manual inventory tracking methods were prone to errors, leading Inventory
1 1990s to stock mismatches, overstocking, and lost revenue. Management]

[Academic Studies on
Early adoption of digital inventory management began with Early Digital Inventory
2 2000s basic Excel-based tracking, but lacked real-time data updates. Systems]

[Research on Cloud
Cloud-based inventory solutions emerged, allowing businesses Computing in
2010- to store, track, and manage stock remotely. However, many Inventory
3 2015 systems lacked integration with sales and supply chain platforms. Management]

AI and IoT started playing a role in inventory automation.

However, small businesses struggled to adopt costly and complex [Case Studies on AI &
4 2018 solutions. IoT in Inventory]

COVID-19 pandemic accelerated the need for automated

inventory tracking due to global supply chain disruptions. [Industry Reports on
2020- Businesses sought smart systems that could predict demand and Supply Chain
5 2021 prevent stockouts. Resilience]

Smart inventory management systems integrating barcode/QR [Market Analysis on

scanning, AI-driven analytics, and cloud databases became Inventory Management
6 2022 essential for businesses to stay competitive. Trends]

Increasing adoption of AI-driven predictive analytics, IoT-based [Recent Research on

real-time stock monitoring, and blockchain for transparency to AI & Blockchain in
7 2024 prevent fraud and inefficiencies. Inventory]

This table provides a clear timeline of how inventory management evolved over the years and highlights
the growing need for smart, automated systems.

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2.2 Existing Solution

Dark patterns are manipulative techniques used by websites and digital platforms to deceive
users into making choices that may not be in their best interest. The widespread concern over
these practices has prompted both regulatory bodies and tech companies to seek solutions to
minimize their impact on users and improve online experiences. Here are some existing
solutions currently in use:
1. Regulations and Laws
Governments worldwide have started to take stronger action against dark patterns, leading to
more robust consumer protection laws.
 GDPR (General Data Protection Regulation): Enforced by the European Union,
GDPR requires companies to obtain explicit consent from users before collecting their
data. It also bans misleading consent forms, a common dark pattern technique. While
GDPR does not directly address all forms of dark patterns, it has pushed businesses to
reconsider their user consent practices and reduce manipulative design.
 California Consumer Privacy Act (CCPA): Similar to GDPR, the CCPA includes
provisions that aim to prevent companies from hiding data consent requests or
tricking users into sharing personal information. The law empowers consumers with
the right to know what data is being collected and to opt out of unnecessary data
sharing.
 FTC Guidelines: The U.S. Federal Trade Commission (FTC) has issued guidelines
on online practices, warning companies against using deceptive designs to manipulate
consumers. The FTC’s policies are helping to shape standards for ethical UX design
across e-commerce, social media, and other digital spaces.
2. Awareness and Reporting Tools
Various organizations have taken steps to raise awareness and provide tools to identify dark
patterns in real-time.
 Dark Patterns.org: Founded by Harry Brignull, who coined the term "dark patterns,"
this website has become a hub for educating consumers about deceptive design
practices. It includes examples of dark patterns, case studies, and a database of reports
submitted by users who have encountered these designs on different websites. It also
serves as a resource for designers to learn how to avoid these unethical tactics in their
own work.
 Browser Extensions: Some developers have created browser extensions aimed at

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detecting dark patterns while users browse. These tools identify manipulative

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 behaviours such as hidden fees or pre-checked subscription boxes. One such extension,
Hawk’s Detector, alerts users in real-time when dark patterns are detected on
websites, providing transparency and enabling users to make informed decisions.
3. Design Solutions and Ethical Design Guidelines
As awareness of dark patterns grows, many design frameworks have emerged to promote ethical
user experiences.
 Ethical Design Frameworks: Designers are adopting ethical design principles to
avoid using manipulative techniques. These principles focus on creating interfaces
that respect user autonomy, provide clear choices, and avoid tricks that could exploit
psychological vulnerabilities. The goal is to improve user trust and satisfaction.
 Inclusive Design Guidelines: Companies like Microsoft and Google advocate for
inclusive design practices that consider the diverse needs of users, including those
who may be vulnerable to dark patterns. This shift toward inclusive design helps
create experiences that empower users rather than manipulate them.
4. Consumer Advocacy and Education
Non-profit organizations and advocacy groups have taken an active role in educating the public
about dark patterns and the importance of fair digital practices.
 Consumer Reports: This organization has been researching and highlighting
deceptive practices in the digital space, including dark patterns. It aims to hold
companies accountable through reports, educational resources, and lobbying for
stricter regulations.
 Educational Campaigns: Several universities, research institutions, and independent
developers are conducting workshops and releasing online resources to train designers
and businesses on ethical design practices. These efforts help spread awareness about
the detrimental effects of dark patterns on consumers.
5. Technology-Driven Solutions
Emerging technologies are being explored to automatically detect dark patterns and prevent them
in digital platforms.
 Machine Learning Models: While some solutions still rely on manual identification,
machine learning models are being developed to detect dark patterns based on
patterns in the data. These models can analyse user interaction data and identify when
a website is manipulating users into specific actions, such as signing up for unwanted
subscriptions.
 Automated Web Testing: Tools like browser automation scripts or testing
frameworks can be programmed to look for specific dark patterns during quality

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assurance testing.

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 These automated systems help ensure that new websites and updates to existing platforms
do not introduce manipulative designs.
2.3 Bibliometric analysis
Bibliometric analysis is a quantitative approach that assesses the research landscape of a specific
topic by examining scholarly literature, citations, authorship patterns, publication trends, and
other metrics. In the context of Hawk’s Detector, a bibliometric analysis provides valuable
insights into the research surrounding dark patterns, web usability, digital ethics, and
detection technologies, illuminating key themes, influential works, and emerging trends in
these domains. The following sections detail the key components and findings of the
bibliometric analysis conducted for the Hawk’s Detector project.

1. Literature Review and Key Terms Identification

 Objective: Identify the primary keywords and themes related to dark patterns, web
usability, deceptive design, browser extensions, and user experience (UX)
manipulation.
 Process: A preliminary literature search in academic databases (e.g., IEEE Xplore,
ACM Digital Library, Google Scholar) was conducted, focusing on terms like “dark
patterns,” “deceptive design,” “manipulative UX,” “ethical web design,” and “real-
time detection.”
 Outcome: The search revealed that literature on dark patterns is emerging rapidly,
especially within UX and web design communities. Key terms identified include
“user manipulation,” “privacy violations,” “deceptive web design,” “consent
engineering,” and “anti-pattern detection.”

2. Publication Trends
 Objective: Analyze trends in publication frequency to determine the level of academic
and industry interest in dark patterns and related technologies.
 Process: Publication data was gathered from various sources, particularly focusing on
papers published over the past decade. Data on the number of publications per year
was extracted to observe growth or decline trends.
 Findings:
o Increasing Interest: There has been a steady increase in publications on dark
patterns, particularly after 2018. This rise is likely due to heightened
awareness around digital privacy and ethics, sparked by regulations like
GDPR and CCPA.
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o Concentrated Growth in UX and Privacy Journals: Journals and conferences

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 focusing on HCI (Human-Computer Interaction), UX design, and digital privacy
are the primary sources of publications, with notable contributions from
venues such as CHI Conference on Human Factors in Computing Systems,
ACM Transactions on Computer-Human Interaction, and Journal of Business
Ethics.

3. Cited References and Influential Works

 Objective: Identify the most frequently cited works in dark pattern and deceptive
design research to understand foundational theories and influential contributions.
 Process: Citation data from academic databases helped pinpoint the most cited papers
and authors, providing a list of seminal works and key contributors to the field.
 Key Influential Works:
o “Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites”
by Gray et al., 2018, is frequently cited for its empirical analysis of dark
patterns across e-commerce websites. This work highlights the prevalence and
types of manipulative tactics online.
o “Deceptive Design in UX: The Ethics of Digital Persuasion” by Harry
Brignull, 2010, is foundational, introducing the term “dark patterns” and
establishing the framework for understanding manipulative UX.
o “Digital Nudging and the Ethical Implications of User Manipulation” by
Weinmann et al., 2016, discusses the ethical aspects of nudging in digital
environments, emphasizing the thin line between persuasion and manipulation.
 Impact on Hawk’s Detector: These works underscore the need for tools like Hawk’s
Detector and provide foundational knowledge for understanding types of dark
patterns, making them essential resources for refining detection criteria and ethical
considerations.

4. Authorship and Collaboration Patterns

 Objective: Examine the authorship landscape, identifying prolific authors and
collaborative networks in dark pattern research.
 Process: By analyzing author data from relevant publications, the analysis highlights
key contributors and collaborative relationships in the field.
 Findings:
o Key Authors: Scholars like Colin M. Gray, Harry Brignull, and Arunesh
Mathur have published influential research on dark patterns, establishing

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themselves as

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 prominent figures in the field.
o Collaborative Networks: Collaboration is common, especially in
interdisciplinary studies involving HCI, psychology, and law. These networks
often involve partnerships between academic institutions and tech companies,
highlighting the practical implications of dark pattern research for industry.
 Impact on Hawk’s Detector: Recognizing these authors and collaborations informs
future research opportunities and helps validate findings through established
expertise. Collaborations with HCI researchers could enhance detection
methodologies and refine ethical considerations.

5. Topic Distribution and Research Themes

 Objective: Identify major research themes and categorize publications according to
their focus areas.
 Process: Using topic modeling and keyword analysis, publications were grouped into
common themes such as detection techniques, ethical implications, case studies, user
awareness, and regulatory responses to dark patterns.
 Main Themes:
o Detection Techniques: Research in this category focuses on methods to
identify and mitigate dark patterns, using algorithms, UX audits, and user
feedback. This aligns directly with Hawk’s Detector’s goal to develop a
detection tool.
o Ethics and User Manipulation: Studies on ethical implications explore the
moral aspects of dark patterns and their impact on user autonomy, a theme
central to Hawk’s Detector’s mission to promote ethical design.
o Case Studies and Empirical Analysis: Case studies analyze the prevalence of
dark patterns across industries, with a focus on sectors like e-commerce and
social media.
o Regulatory and Legal Perspectives: A growing body of work examines how
regulations address dark patterns, providing insights into compliance
considerations for tools like Hawk’s Detector.
 Impact on Hawk’s Detector: Understanding these themes has helped shape the
extension’s features, such as focusing on detection methods that highlight ethical
concerns and incorporating compliance with privacy laws.

6. Methodological Approaches in Dark Pattern Detection

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 Objective: Review and classify the methodologies used in dark pattern research to

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 inform Hawk’s Detector’s detection approach.
 Process: Publications were analyzed based on their methodological focus,
categorizing them as empirical studies, algorithmic detection methods, qualitative
analyses, or case studies.
 Key Findings:
o Algorithmic and Rule-Based Detection: Most detection-oriented studies
employ algorithmic methods or rule-based approaches, utilizing DOM
analysis, pattern matching, and NLP techniques. This supports Hawk’s
Detector’s use of DOM manipulation and regex to identify dark patterns.
o User Studies and UX Evaluation: A number of studies utilize user testing and
UX evaluations to understand the impact of dark patterns on user behavior,
which provides valuable context for enhancing Hawk’s Detector’s user
education features.
 Impact on Hawk’s Detector: Insights from algorithmic studies have validated the use
of DOM manipulation and regex, while user study findings emphasize the importance
of a user-centered interface that informs users about detected patterns.

7. Impact of Regulations and Policy Studies on Dark Patterns

 Objective: Examine how regulatory studies address dark patterns, with an emphasis
on privacy laws and compliance frameworks.
 Process: Regulatory-focused publications were reviewed, particularly studies on
GDPR, CCPA, and other data protection laws, to understand their implications for
dark pattern detection tools.
 Findings:
o GDPR and CCPA Implications: Both GDPR and CCPA emphasize the need
for transparency and fair user treatment, setting a precedent for limiting
manipulative designs. Studies highlight how these laws indirectly discourage
dark patterns and support ethical design practices.
o Guidelines and Compliance Tools: Some studies provide compliance
frameworks for developers, suggesting best practices to avoid or detect dark
patterns within legal guidelines.
 Impact on Hawk’s Detector: Incorporating these regulatory insights has guided
Hawk’s Detector’s privacy-first approach, ensuring that the extension’s features are
fully compliant with data protection laws and user privacy expectations.

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8. Emerging Research Areas and Future Directions
 Objective: Identify emerging areas in dark pattern research to forecast future
directions and potential areas for Hawk’s Detector’s development.
 Findings:
o AI and Machine Learning for Advanced Detection: While current detection
methods rely on DOM and regex analysis, studies indicate an emerging
interest in AI for more sophisticated pattern recognition, suggesting future
directions for Hawk’s Detector’s evolution.
o Crowdsourced Databases and Community Reporting: Recent studies explore
crowdsourced dark pattern databases, which could allow users to report new
patterns, fostering a community-driven approach to detection.
o Mobile and Multiplatform Detection: A shift toward mobile web use has
spurred research on dark patterns in mobile interfaces, indicating a need for
multiplatform compatibility.
 Impact on Hawk’s Detector: These trends suggest potential feature expansions, such
as AI-driven detection, community reporting, and support for mobile browsers, all of
which align with the goal of continuous improvement and adaptability.

9. Citation Metrics and Impact Analysis

 Objective: Examine citation metrics to identify the most impactful works and
determine citation trends over time, indicating the increasing relevance of dark pattern
research.
 Process: Citation counts were extracted from leading academic databases (e.g.,
Google Scholar, Scopus) to identify works with high citation rates and analyze their
influence in shaping discourse on dark patterns.
 Findings:
o High Citation Growth Rate: The past five years have shown a marked increase
in citations of dark pattern research papers, indicating a growing academic and
industry interest in user ethics and deceptive design.
o Influential Authors and Studies: Papers by Brignull, Gray, and Mathur have
consistently high citation counts, establishing them as foundational references.
Studies focusing on the ethical implications of UX, along with empirical
analyses of dark patterns in e-commerce, are particularly influential.
 Impact on Hawk’s Detector: Highly-cited works reinforce the importance of certain
detection criteria (e.g., misleading design and ambiguous options), shaping the types

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of patterns prioritized in Hawk’s Detector. High-citation topics such as ethical UX
design

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 also underscore the tool's focus on promoting ethical user practices.

10. Regional Distribution and Focus Areas

 Objective: Assess regional trends in dark pattern research to understand how different
regions approach digital ethics and dark patterns, with a focus on privacy laws and
cultural perspectives.
 Process: Using geographical data from published studies, regional patterns in research
outputs were analyzed to observe how cultural and legal factors affect dark pattern
research.
 Findings:
o Prominent Regions in Research: North America and Europe are the primary
contributors to dark pattern research, likely influenced by strict digital privacy
regulations such as GDPR (Europe) and CCPA (California, USA).
o Emerging Regions: Asia and Australia show growing interest, especially
around privacy-focused design and online consumer rights, as countries in
these regions are increasingly adopting data protection frameworks.
o Region-Specific Dark Patterns: Regional studies suggest that certain dark
patterns are more common in specific areas. For example, Asia shows higher
instances of patterns related to data collection without consent, while Europe
focuses on user consent manipulation to comply with GDPR.
 Impact on Hawk’s Detector: Regional insights highlight the need for adaptable
detection features that cater to different regulatory and cultural norms. Future versions
of Hawk’s Detector could incorporate region-specific alerts, ensuring relevance for
users in diverse locales.

11. Collaboration Networks and Interdisciplinary Studies

 Objective: Map out collaboration networks among researchers to identify
interdisciplinary partnerships and shared contributions across fields such as UX
design, digital ethics, psychology, and law.
 Process: Co-authorship and funding sources for published research were analyzed to
reveal collaborative efforts and the interdisciplinary nature of dark pattern research.
 Findings:
o Interdisciplinary Focus: There is substantial interdisciplinary collaboration,
notably between HCI (Human-Computer Interaction) experts, psychologists,
legal scholars, and data privacy researchers. This blend of expertise enhances

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 the depth of analysis and understanding of dark patterns from both behavioral and
ethical perspectives.
o Institutional and Industry Partnerships: Many studies are funded by both
universities and tech companies, with some corporations contributing data to
understand consumer manipulation. Additionally, policy think tanks contribute
to regulatory insights and ethical standards.
 Impact on Hawk’s Detector: Collaborating across fields can enrich the feature
development process, especially regarding legal compliance, psychological
implications, and UX best practices. Interdisciplinary insights offer valuable ideas for
potential partnerships that could further enhance Hawk’s Detector’s effectiveness and
adaptability.

12. Impact of Regulatory Research and Compliance Frameworks

 Objective: Investigate regulatory research to determine how compliance frameworks
guide the design and functionality of dark pattern detection tools.
 Process: Studies focusing on regulatory frameworks (e.g., GDPR, CCPA) and dark
patterns were reviewed to understand the legal context for ethical design and
manipulation avoidance.
 Findings:
o Legal Implications for Detection Tools: Research highlights the complexity of
complying with various data privacy laws when detecting dark patterns, as
each law defines user consent and transparency differently. Hawk’s Detector
benefits from this by focusing on local-only data processing to minimize
privacy concerns.
o Evolving Policies: Some studies predict that data protection regulations will
increasingly address dark patterns directly, potentially requiring companies to
disclose manipulation tactics in their UX. This presents an opportunity for
Hawk’s Detector to serve as a compliance aid, helping organizations identify
and rectify non-compliant design elements.
 Impact on Hawk’s Detector: Awareness of regulatory requirements has influenced
Hawk’s Detector’s privacy-first design, ensuring that the extension complies with
current regulations and is adaptable to future policy shifts. Moreover, these insights
underscore the potential to position Hawk’s Detector as a compliance-oriented tool for
businesses.

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13. Funding and Institutional Support Patterns
 Objective: Examine funding sources for dark pattern research to understand which
organizations and sectors support this area and their motivations.
 Process: Funding acknowledgments in dark pattern research papers were analyzed to
identify prominent funding sources and their contributions to the field.
 Findings:
o Academic and Government Funding: A significant portion of research is
funded by academic institutions and government grants, with programs
focused on digital ethics and consumer protection. This support reflects public
interest in mitigating manipulation and promoting ethical design.
o Private Sector and Nonprofit Funding: Some funding comes from tech
companies and nonprofits focused on data rights and online consumer
advocacy, which highlights an industry interest in understanding and reducing
manipulative practices.
 Impact on Hawk’s Detector: Recognition of industry funding for ethical design
highlights a potential avenue for partnerships. Hawk’s Detector could explore
alliances with nonprofits or advocacy groups to expand its reach and potentially gain
sponsorships, facilitating further development and enhancements.

14. Future Research Directions and Hawk's Detector Potential Expansions

 Objective: Forecast future trends in dark pattern research to explore how Hawk’s
Detector can evolve to meet emerging needs in detection and ethical digital practices.
 Emerging Focus Areas:
o Integration of Artificial Intelligence and Machine Learning: Research
increasingly points toward using AI for more advanced pattern detection,
enabling dynamic detection of complex patterns beyond rule-based methods.
o Mobile and Smart Device Compatibility: With more users accessing the web
via mobile devices, research is trending toward dark pattern detection on
smaller screens. Adapting Hawk’s Detector for mobile browsers could open
new user segments and address mobile-specific manipulation tactics.
o Crowdsourced Reporting and Community Databases: Crowdsourced platforms
for user-reported dark patterns are gaining traction, enabling tools like Hawk’s
Detector to harness community insights for pattern updates and collaborative
detection efforts.
o Real-Time Compliance Monitoring for Businesses: Future research indicates a

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 potential demand for tools that offer real-time compliance checks for businesses,
allowing them to detect and resolve dark patterns in their UX designs
proactively.
 Impact on Hawk’s Detector: These trends suggest opportunities for Hawk’s Detector
to integrate AI capabilities, launch mobile-compatible versions, incorporate
community reporting, and offer compliance-monitoring features, aligning the
extension with industry advancements and enhancing its utility and user engagement.

15. Insights on Ethical Design Principles and Digital Literacy

 Objective: Explore research on ethical design to understand how it influences users’
digital literacy and how detection tools can promote awareness.
 Findings:
o Ethical Design Education: Studies show that ethical design can be part of
digital literacy initiatives, helping users recognize manipulative tactics and
make informed online choices. Research advocates for tools that not only
detect but educate, aligning with Hawk’s Detector’s educational focus.
o User Empowerment and Digital Autonomy: Research underlines the
importance of empowering users by offering tools that enhance their
autonomy. This aligns with Hawk’s Detector’s goal of equipping users with
the knowledge and tools to navigate the web responsibly and avoid
manipulation.
 Impact on Hawk’s Detector: Integrating ethical design principles and educational
content can enhance user awareness and empower users to make informed choices.
This aligns Hawk’s Detector with digital literacy efforts, potentially positioning it as a
valuable tool for both detection and user education.

The bibliometric analysis of dark pattern research reveals an evolving, interdisciplinary field
marked by rising academic and industry interest, regulatory support, and a strong ethical
focus. This analysis underscores Hawk’s Detector’s relevance in providing a much-needed
solution to detect and educate users about dark patterns while respecting privacy regulations
and ethical standards. Future research areas such as AI-driven detection, crowdsourced
reporting, and mobile compatibility highlight paths for Hawk’s Detector’s ongoing
development, positioning it to stay at the forefront of dark pattern detection and digital ethics
advocacy.

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2.4 Review Summary
A browser extension designed to detect dark patterns on websites. This review summary
covers the project’s purpose, technical approach, innovative elements, strengths, and potential
areas for improvement based on similar initiatives and user feedback in the field of dark
pattern detection.

Project Purpose and Relevance

Hawk’s Detector addresses a critical need in the digital landscape, where manipulative design
practices, commonly known as dark patterns, can mislead users into making unintended
choices. These patterns are increasingly prevalent on e-commerce websites, social media
platforms, and mobile applications, often prioritizing business goals over user autonomy and
ethical transparency. The project aligns with the broader push for digital consumer rights, as
seen with regulatory measures like GDPR, which mandate ethical design practices.
The project’s objective is to empower users to recognize and navigate these dark patterns by
providing real-time notifications when manipulative elements are detected. It aims to foster a
safer browsing environment and promote awareness around dark patterns, ultimately
contributing to a more user-centric digital experience.’

Technical Approach and Key Features

The extension’s functionality is built on JavaScript, DOM manipulation, and regular
expressions (regex), providing a robust and effective approach to real-time dark pattern
detection. Here’s a breakdown of its main features and technical highlights:
 Real-Time Detection: The extension actively monitors web pages as the user
browses, detecting dark patterns such as hidden fees, forced continuity, and deceptive
button choices. By providing immediate notifications, Hawk’s Detector enhances user
awareness at critical decision points.
 DOM Manipulation: Using the Document Object Model (DOM) to access and
analyze webpage elements allows the extension to interact with various web page
components effectively. DOM manipulation is central to highlighting dark patterns,
making it easier for users to recognize manipulative tactics.
 Regular Expressions (Regex): Regex enables efficient pattern matching across web
pages, allowing the extension to scan for known deceptive practices without relying
on more resource-intensive methods like machine learning models. This approach
ensures a streamlined, user-friendly experience.
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  Cross-Browser Compatibility and Privacy Considerations: The extension is
designed to function across different browsers and platforms without storing or
sharing personal data, aligning with privacy-first principles crucial for user trust.

Innovative Elements
Hawk’s Detector stands out for its simplicity and accessibility. Unlike other solutions that rely on
complex machine learning algorithms, this extension keeps the process straightforward,
minimizing computational load and enhancing ease of use. This design decision is
advantageous for users who may be concerned about privacy or who prefer lightweight
applications that don’t impact browser performance.
Additionally, the extension’s cross-platform support broadens its reach and accessibility, making
it suitable for users on various devices and browsers.

Strengths
1. User-Focused Design: The extension’s emphasis on usability and transparency
makes it accessible to a broad user base, including non-technical individuals who may
be less familiar with deceptive design practices.
2. Real-Time Feedback: By notifying users in real-time, Hawk’s Detector provides
immediate value, empowering users to make informed decisions at the moment of
interaction.
3. Ethical and Privacy-Conscious Approach: The decision not to use machine
learning models enhances privacy, as the extension only accesses necessary data
without storing or analyzing personal information.
4. Practical and Cost-Effective: The focus on using regex and DOM manipulation
makes the extension more efficient and avoids the complexity and cost of ML model
training, making it a cost-effective and easily maintainable solution.
Areas for Improvement and Future Opportunities
While Hawk’s Detector is effective for real-time detection and basic reporting, there are areas
where future iterations could enhance functionality:
1. Expanded Dark Pattern Database: Incorporating an expanding repository of known
dark patterns, updated regularly, could improve detection accuracy and help users stay
aware of emerging tactics.
2. Enhanced User Reporting Mechanism: Allowing users to contribute by reporting
dark patterns they encounter could provide valuable feedback, helping improve
detection algorithms and tailor the extension to evolving user needs.

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 3. Educational Content: Adding educational resources or a brief overview within the
extension on common dark patterns could further empower users, helping them
understand the implications of these tactics beyond notifications.

4. Collaboration with Regulatory Bodies: Partnering with consumer protection agencies

could help standardize detection criteria and potentially influence policy around
deceptive practices online.

Hawk’s Detector effectively addresses the need for a practical, accessible solution to the growing
issue of dark patterns in digital spaces. Its user-focused design, real-time detection
capabilities, and ethical stance on privacy position it as a valuable tool for consumers seeking
more control over their online experience. As the project evolves, incorporating additional
educational features and a user-reporting mechanism could enhance its impact, making it a
comprehensive solution in the fight against manipulative design practices.

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2.5 Problem Definition
1. Background of Dark Patterns
In the digital age, users increasingly interact with websites and applications for various
purposes, from shopping to social networking and information retrieval. Unfortunately,
alongside this growth in digital services, there has been an increase in the use of "dark
patterns"
— deceptive design techniques intentionally embedded in user interfaces (UI) to manipulate
user behavior. Dark patterns are crafted to confuse, mislead, or coerce users into actions that
they may not have taken otherwise, such as accidentally signing up for subscriptions, sharing
unnecessary personal information, or making unintentional purchases.
Dark patterns exploit human psychology, using tactics that benefit service providers at the
expense of user autonomy, trust, and transparency. These manipulative designs can erode
user trust, violate privacy, and lead to a degraded user experience, undermining ethical
standards and potentially violating regulations such as the General Data Protection
Regulation (GDPR).

2. Types and Examples of Dark Patterns

Dark patterns are varied in form and application, with some of the most common types
including:
 Hidden Costs: Unexpected charges added at the final stages of checkout, leading
users to spend more than intended.
 Forced Continuity: Subscriptions that automatically renew without clear reminders
or easy cancellation options, trapping users in unintended payments.
 Privacy Zuckering: Subtle design tricks that lead users to share more personal data
than they would knowingly consent to.
 Bait and Switch: Misleading users to click on a button with the expectation of one
result, only to experience something entirely different.
 Trick Questions: Language or formats in forms that confuse users, causing them to
unknowingly select options they wouldn’t otherwise choose.
Each of these patterns has a direct impact on user trust and ethical transparency in digital
services, posing challenges to both consumers and digital platforms aiming for ethical design.

3. Impact of Dark Patterns on Users

Dark patterns create significant issues for users, including:
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 Financial Impact: Hidden costs and forced subscriptions lead to unwanted expenses
and financial loss for users.

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  Privacy Concerns: Many dark patterns manipulate users into sharing personal data
without informed consent, infringing upon user privacy.
 Decreased Trust in Digital Platforms: Repeated encounters with dark patterns erode
trust, leading users to be wary of digital interactions and less willing to share data or
make online purchases.
 Psychological Frustration and Cognitive Load: Deceptive designs can increase
cognitive load, causing frustration and creating a negative digital experience.
These impacts highlight the need for solutions that empower users to recognize and avoid dark
patterns, enabling more informed decisions.

4. Challenges in Detecting and Mitigating Dark Patterns

Detecting dark patterns is challenging for several reasons:
 Complexity and Subtlety: Dark patterns are often subtle and can vary widely in
implementation. Detecting them requires careful examination of user interface
elements, often involving subjective analysis.
 Dynamic Web Content: Websites are increasingly using dynamic content that
changes based on user behavior or personal data, making it difficult to detect and
analyze patterns reliably.
 Lack of Standardization: There is no single, universally accepted definition or
classification of dark patterns, making automated detection complex and imprecise.
 Privacy Concerns in Detection Tools: While detecting dark patterns, tools must
respect user privacy and avoid collecting or transmitting personal data.
These challenges highlight the technical and ethical complexity of designing tools that detect
dark patterns effectively.

5. Need for Hawk's Detector

Given the background, impact, and challenges associated with dark patterns, Hawk's Detector
was developed to address this problem by providing users with a practical tool to identify
dark patterns in real time. Hawk’s Detector aims to:
 Empower Users: Provide users with the ability to detect and recognize manipulative
tactics, enabling them to make informed choices and regain control over their digital
interactions.
 Promote Ethical Design: Help designers and developers understand the ethical
implications of user experience (UX) choices, encouraging transparency and ethical

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 standards in digital design.

 Enhance Digital Literacy: By highlighting dark patterns, Hawk’s Detector educates

users on manipulative tactics, fostering digital literacy and critical thinking.
 Respect User Privacy: Ensure that detection occurs locally within the browser, with
no data collection or external transmission, aligning with privacy regulations and
maintaining user trust.

6. Research Questions and Goals

To address the problem of dark patterns effectively, Hawk’s Detector seeks to answer the
following research questions:
 RQ1: How can we accurately identify and categorize dark patterns across diverse
websites and interfaces in real time?
 RQ2: What techniques can be used to detect dark patterns without compromising user
privacy?
 RQ3: How can Hawk’s Detector be designed to be user-friendly and accessible to
non- technical users?
 RQ4: What are the most common types of dark patterns affecting users today, and
how can detection features be tailored to address these specific patterns?
Through these questions, Hawk’s Detector aims to enhance user autonomy, empower ethical UX
design, and contribute to a fairer and more transparent digital experience for all users.

7. Scope of the Problem and the Role of Hawk's Detector

The scope of dark patterns spans across various digital interfaces and platforms, from e-
commerce to social media and beyond. As new patterns emerge and evolve, tools like Hawk's
Detector play a crucial role in adapting to these changes by identifying, categorizing, and
educating users on manipulative designs. The extension focuses on detecting common
patterns through browser-based analysis, providing real-time notifications, and raising
awareness of deceptive design practices.
Hawk’s Detector is thus positioned as a practical, privacy-conscious solution that not only
mitigates the effects of dark patterns but also promotes a broader movement toward
transparency, ethical design, and user empowerment in the digital space.

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2.6 Goals/Objective
Hawk's Detector was conceived to address the growing prevalence of "dark patterns" on
websites—design techniques that manipulate users into unintended actions, often benefiting
companies at users' expense. The extension's primary goal is to empower users to recognize
and avoid these deceptive practices by providing a tool that detects and highlights dark
patterns in real time. Below, the goals and objectives are detailed to show how each
contributes to the overarching aim of promoting a more ethical and user-centered web
experience.

1. Primary Goal: Detect and Notify Users of Dark Patterns in Real Time
 Objective: Build an extension that effectively identifies a wide range of dark patterns
(e.g., misleading buttons, forced consent, hidden fees).
 Details: Hawk’s Detector scans web pages in real time, using DOM manipulation and
regular expressions (regex) to recognize manipulative design elements like deceptive
buttons, ambiguous choices, and hidden information. By detecting these patterns as
users navigate, the tool not only warns users of potential traps but also educates them
on deceptive techniques.
 Success Metrics: The extension’s detection capabilities are evaluated based on the
accuracy and breadth of detected patterns, along with user feedback indicating
satisfaction with its real-time alerts.

2. Ensure Privacy and Data Security for Users

 Objective: Process all data locally without storing or transmitting any user data to
external servers, ensuring compliance with privacy laws (e.g., GDPR, CCPA).
 Details: To earn users' trust, Hawk’s Detector prioritizes local-only processing. This
means that no personal data or browsing history is collected, stored, or sent elsewhere.
Instead, all analysis and detection occur within the user’s browser, reducing privacy
concerns and potential security risks.
 Success Metrics: Compliance with privacy regulations, positive user feedback
regarding data security, and a transparent privacy policy are key indicators of success.

3. Provide an Accessible and User-Friendly Interface

 Objective: Design an intuitive and accessible user interface that can be used by
individuals with varying levels of technical expertise and accessibility needs.
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 Details: The extension features a clean, straightforward interface with simple visual

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 cues for dark patterns, user-friendly settings, and optional customization. Accessibility
features are incorporated to cater to users with visual impairments (e.g., screen reader
support, high-contrast modes).
 Success Metrics: User adoption rates and feedback on usability, along with high
satisfaction levels from users of all skill levels, signal the interface’s effectiveness.
Positive feedback from users with accessibility needs indicates success in inclusivity.

4. Educate and Empower Users to Recognize Dark Patterns Independently

 Objective: Increase users' awareness of dark patterns, helping them learn to recognize
manipulative tactics even outside the scope of the extension.
 Details: The extension includes educational resources, such as a “Learn More”
section, that explains different types of dark patterns and offers real-life examples.
This feature aims to enhance users’ knowledge and encourage critical thinking while
browsing.
 Success Metrics: Engagement metrics, such as the frequency of users accessing
educational resources, alongside positive feedback on increased awareness, reflect the
success of the educational component.

5. Promote Ethical Design and Transparency in Digital Spaces

 Objective: Encourage a shift toward ethical design practices by raising awareness
about dark patterns and their negative impacts on user trust and experience.
 Details: Hawk’s Detector indirectly promotes digital fairness by bringing attention to
the prevalence of dark patterns, nudging companies to adopt more user-centered,
ethical design choices. By creating an environment where dark patterns are more
visible, the tool aims to discourage their use over time.
 Success Metrics: An increase in the adoption of ethical design practices across
websites, as measured by user reports and feedback, would indicate a positive industry
shift influenced by the extension.

6. Enable Cross-Browser Compatibility for a Broad User Base

 Objective: Design the extension to function consistently across popular web browsers
(e.g., Chrome, Firefox, Edge) to maximize accessibility.
 Details: To reach as many users as possible, Hawk’s Detector is developed to work
across multiple browsers through the Web Extensions API, which standardizes
functionality. Cross-browser compatibility ensures that users on various platforms can

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access the tool without barriers.

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  Success Metrics: The successful deployment and stable performance on multiple
browsers demonstrate cross-browser compatibility, while feedback from users across
different platforms confirms accessibility.

7. Optimize Performance for Minimal Resource Consumption

 Objective: Ensure that Hawk’s Detector operates efficiently, with low CPU and
memory usage, to avoid slowing down users’ browsing experience.
 Details: Real-time dark pattern detection can be resource-intensive, so Hawk’s
Detector is optimized to be lightweight and efficient. It leverages minimal scripts and
streamlined code, running background processes only when needed.
 Success Metrics: The extension’s performance can be evaluated by analyzing its CPU
and memory usage metrics on various devices, with low resource consumption
signifying success. User feedback on the absence of lag or interference with browsing
also indicates performance optimization.

8. Enable Future Expandability for Additional Features

 Objective: Design a modular architecture that allows future updates and additional
features, such as community reporting or AI-based detection.
 Details: The project aims to build a flexible, scalable architecture so that future
features can be added easily. Planned features include the possibility of integrating AI
detection for advanced patterns, a crowdsourced reporting system for emerging
patterns, and mobile browser support.
 Success Metrics: The modularity and scalability of the extension’s code can be
measured by the ease with which new features are incorporated, along with the
extension’s adaptability to accommodate updates without performance degradation.

9. Build Trust Through Transparency and Ethical Practices

 Objective: Maintain transparency with users regarding the extension’s functionality,
limitations, and privacy practices, fostering trust and accountability.
 Details: Hawk’s Detector upholds ethical principles by being clear about what the
extension does and doesn’t do. Its privacy policy explicitly states that no data is
collected or shared, and a straightforward FAQ section clarifies its capabilities and
limitations.
 Success Metrics: Positive user feedback on transparency and user trust, as well as a
low churn rate due to clarity around privacy and usage practices, reflect success in

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building.

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 CHAPTER 3
DESIGN FLOW/ PROCESS
3.1 Evaluation & Selection of specification/features
In the development of Hawk’s Detector, careful consideration was given to the specifications and
features to ensure the tool effectively meets user needs without sacrificing performance or
privacy. Below is an evaluation of the selected features:

Real-Time Detection with DOM Manipulation and Regex: Real-time detection was selected
as a priority to give users immediate feedback about dark patterns as they browse websites.
The decision to use DOM manipulation allows for precise interaction with the structure of
web pages, ensuring that deceptive elements can be identified and highlighted dynamically.
The use of regular expressions (regex) provides a powerful yet efficient way to scan for
specific patterns and manipulative content, minimizing processing load while maintaining
accuracy.

User Privacy and Minimal Data Access: Since user trust is paramount, the extension was
designed to access only the necessary elements for detecting dark patterns. By not collecting
or storing personal data, the extension aligns with privacy concerns, fostering user
confidence. This feature was chosen over more invasive approaches that could risk
compromising user security or require extensive permissions.

Cross-Browser Compatibility: To maximize its reach, the extension supports multiple

browsers. This feature was selected to ensure that Hawk’s Detector can serve a diverse user
base, from Chrome to Firefox and other platforms. It ensures that users are not restricted by
their choice of browser, making the extension adaptable and inclusive.

User-Friendly Interface: A simple and intuitive interface was prioritized to ensure the tool
remains accessible to all users, regardless of technical skill. The design of the interface
focuses on clarity, with straightforward notifications and reports that avoid technical jargon.
This approach was selected to ensure that even non-technical users can easily interact with
and understand the findings.

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Avoidance of Machine Learning Models: Rather than implementing complex machine
learning models, the decision was made to use more traditional programming methods (DOM
manipulation and regex). This choice was driven by the goal of reducing both the
computational overhead and the learning curve for users, ensuring a fast, responsive
experience that doesn’t compromise on simplicity. Users benefit from quick installations and
immediate results without the need for training datasets or model tuning.

Transparent Reporting of Detected Dark Patterns: Transparency is essential for user

awareness. The extension provides clear reports of detected dark patterns, explaining how
each pattern attempts to manipulate the user. This feature allows users to make informed
decisions and empowers them with the knowledge needed to recognize deceptive practices.
The reporting structure was selected to build user understanding, contributing to overall
digital literacy.

Non-Intrusive Browsing Experience: Ensuring that the extension does not interfere with the
natural browsing experience was a key factor. The team chose not to block any web content
or hinder the layout of the website, allowing users to continue their normal activities while
receiving discreet alerts about dark patterns. This decision was made to balance functionality
with user experience, making the extension both effective and non-intrusive.

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3.2 Design Constraints

Design constraints for Hawk's Detector define the boundaries within which the project
operates, influencing the tool's usability, functionality, and compliance. These constraints
stem from performance requirements, privacy concerns, regulatory obligations, and
compatibility limitations. The following sections detail each design constraint and its impact
on the extension’s development and final structure.

3.2.1 Performance Constraints

1. Efficient Resource Usage on User Devices
o Description: Since Hawk’s Detector is designed to operate in real-time, the
extension needs to run efficiently without straining system resources,
especially on low-powered devices such as laptops and mobile devices.
o Impact: To avoid draining CPU and memory, complex detection algorithms
and resource-intensive processes (such as constant DOM monitoring) were
avoided. Lightweight code and minimal scripts were prioritized to ensure the
extension could work smoothly even with limited system resources.
o Implementation Choices: DOM manipulation and regex were chosen over
machine learning or deep analysis, as they are faster and consume fewer
resources. Further, the extension’s code was optimized to avoid unnecessary
re- renders, reducing performance impacts on dynamic web pages.
2. Low Latency for Real-Time Feedback
o Description: Hawk’s Detector must analyze and provide feedback quickly, so
users receive notifications without noticeable delay. Slow detection could lead
to a poor user experience.
o Impact: Real-time detection demands high-speed code execution and a rapid
response time. To ensure low latency, heavy computations were minimized.
o Implementation Choices: Detection scripts are optimized to load only when
necessary, and non-essential background tasks are minimized. This approach
balances real-time detection accuracy with the need to avoid browser lag,
especially when users are actively interacting with a website.

3.2.2 Privacy and Security Constraints

1. Local Processing for Enhanced Privacy
o Description: The extension must process all data locally on the user’s device

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 to meet privacy expectations and regulatory standards. No browsing data or user
interactions should be sent to external servers.
o Impact: Local-only processing limits the ability to use cloud-based machine
learning or external data storage for advanced detection, which could
otherwise improve detection accuracy through shared learning.
o Implementation Choices: All detection processes are handled locally using
in- browser functions and DOM analysis. To reinforce trust, a privacy policy
clearly outlines that no data is shared externally, aligning with GDPR and
CCPA guidelines. This constraint required strict local processing, thus limiting
certain feature expansions that would involve remote data handling.
2. Compliance with GDPR, CCPA, and Other Privacy Regulations
o Description: The extension must adhere to data privacy regulations such as
GDPR (General Data Protection Regulation) and CCPA (California Consumer
Privacy Act) to ensure it does not infringe on user rights.
o Impact: Compliance requires that the extension does not collect or store any
identifiable data from the user’s browsing session. All data processing must be
anonymous and local to avoid legal complications.
o Implementation Choices: No personal data is logged, stored, or shared by the
extension. To comply, user actions and preferences are limited to local
storage, and no tracking mechanisms are used. This constraint restricts
advanced user analytics and feedback gathering but ensures full regulatory
compliance.

3.2.3 Cross-Browser Compatibility Constraints

1. Consistency Across Major Browsers
o Description: The extension must operate consistently on major browsers,
particularly Chrome, Firefox, and Edge, as each browser has unique API
handling and extension standards.
o Impact: Ensuring cross-browser compatibility requires writing code that
aligns with each browser’s Web Extensions API. Browser-specific differences
can affect performance, particularly in DOM manipulation and notification
handling.
o Implementation Choices: A single codebase was developed using
standardized APIs, with slight adaptations for each browser as needed. Testing
across multiple browsers identified discrepancies early, allowing for

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adjustments to achieve uniform performance and functionality across
platforms.

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 2. Limitations of Web Extensions API
o Description: The extension is constrained by the features supported by the
Web Extensions API, which dictates the extent of what can be done within the
browser environment.
o Impact: Some detection techniques or advanced interactive features are
limited by the capabilities and permissions of this API. For instance, there are
restrictions on background scripts and page access that affect real-time DOM
manipulation.
o Implementation Choices: Within API constraints, Hawk’s Detector limits
permissions to only those essential for dark pattern detection, ensuring
minimal disruption to user experience. Additionally, background tasks were
streamlined to meet API limitations without sacrificing detection quality.

3.2.4 User Experience and Usability Constraints

1. Minimal User Input Requirement
o Description: The design must ensure that the extension operates with minimal
user input, catering to a wide range of users, including those without technical
expertise.
o Impact: Simplifying the interface and automating functions (like notifications
and visual cues) is essential for user accessibility. Complex setup requirements
or highly customizable features could alienate non-technical users.
o Implementation Choices: An intuitive interface with automated notifications
and simple visual highlighting was adopted. Customization options are limited
to essential settings like notification preferences, ensuring the extension remains
accessible to a general audience without unnecessary complexity.
2. Non-Intrusive Design
o Description: The extension must be non-intrusive, so it doesn’t interfere with
the user’s browsing experience, particularly during web page interaction.
o Impact: Overly aggressive alerts or visually distracting elements would
compromise the extension’s usability, so it was essential to strike a balance
between detection notifications and a subtle user interface.
o Implementation Choices: Real-time notifications are non-intrusive and
customizable, and flagged elements are highlighted subtly to avoid
interrupting the user’s workflow. This careful design preserves the browsing
experience while delivering effective, clear alerts when dark patterns are

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detected.

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 3.2.5 Technical and Scalability Constraints
1. Limited Processing Power on Low-End Devices
o Description: Many users may run the extension on low-power or resource-
constrained devices, like older laptops or Chromebooks, so efficient
processing is crucial.
o Impact: Heavy computation or extensive page scanning could lead to
slowdowns, making the extension unusable on some devices.
o Implementation Choices: Lightweight scripts were developed to minimize
resource usage, optimizing for performance on a range of devices. Heavy
detection processes were avoided in favor of efficient DOM scanning
methods, ensuring that the tool remains responsive even on low-power
devices.
2. Modular and Scalable Code Architecture
o Description: The design must allow for future feature expansions (such as
additional dark pattern categories) without needing a complete rewrite.
o Impact: The need for scalability required careful planning of the code
structure, allowing for modularity without overly complicating the initial
release.
o Implementation Choices: A modular architecture was implemented, enabling
additional features or updates to be added without affecting core functionality.
This forward-thinking design ensures the extension can evolve with user needs
and regulatory changes.

Hawk's Detector's design constraints required balancing high performance, privacy, and ease of
use with cross-browser compatibility and regulatory compliance. These constraints
influenced core design choices, from selecting lightweight detection methods and local
processing to simplifying the user interface. Each decision aligns with the extension’s goals
of effective dark pattern detection, user trust, and accessibility across a wide audience,
positioning Hawk’s Detector as a reliable, ethical, and user-centered solution for navigating
deceptive design practices.

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3.3 Analysis of features and finalization subject to constraints

The development of Hawk's Detector required a thoughtful approach to feature selection,

prioritizing functionality that meets user needs while balancing technical, resource, and
regulatory constraints. This section details the analysis of core features, evaluating their
impact on usability, effectiveness, and compliance, and explaining how they were finalized
within project limitations.

1. Feature Analysis and Prioritization

Each feature was analysed to determine its importance to the core objectives of Hawk’s
Detector, namely, to provide a reliable, real-time detection tool for dark patterns without
compromising user privacy or performance. The prioritized features include real-time
detection, notifications, visual highlighting, and privacy assurance, which are critical for user
trust and effective dark pattern identification.

i. Real-Time Detection

o Purpose: Real-time detection is essential for providing immediate feedback to

users, allowing them to respond to dark patterns as they encounter them.

o Analysis: This feature is central to the tool’s functionality, as delayed or post-

visit notifications would reduce its impact and potentially frustrate users.

o Constraints: Real-time analysis demands efficient code and resource usage. To

avoid excessive memory and CPU load, the detection algorithms had to be
optimized, leading to the decision to use lightweight regex and DOM
manipulation instead of resource-intensive machine learning.

o Finalization: Real-time detection was retained as a core feature but limited to

avoid significant performance costs, especially on low-powered devices.

ii. Notifications

o Purpose: Immediate notifications ensure that users are informed about

manipulative elements without needing to check the extension manually.

o Analysis: User testing indicated that real-time notifications were highly

effective in raising awareness. However, overly frequent alerts were perceived
as intrusive and could lead to alert fatigue.
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 o Constraints: Notifications had to be managed carefully to avoid overwhelming
users. Customization options were considered to allow users to control alert
frequency.

o Finalization: Notifications were retained as an adjustable feature, allowing

users to select alert intensity based on personal preference and tolerance for
interruptions.

iii. Visual Highlighting of Deceptive Elements

o Purpose: Highlighting elements helps users easily locate manipulative items

on a page, increasing the transparency of dark patterns.

o Analysis: This feature proved useful in testing, as it helped users understand

why elements were flagged. However, complex highlighting (e.g., animations
or extensive styling) could impact page loading times.

o Constraints: Implementing unobtrusive but effective visual cues required

balancing visual impact with loading performance, as intensive styling could
hinder page rendering, especially on large websites.

o Finalization: A simple but distinct color overlay was chosen, which does not
alter the layout but remains visible enough to capture attention without
negatively affecting load times.

iv. Privacy-Centered Design

o Purpose: Protecting user privacy by ensuring all data processing occurs locally
on the user’s device, in compliance with regulations like GDPR.

o Analysis: Privacy was identified as a non-negotiable feature, as dark pattern

detection itself aims to uphold user rights and transparency.

o Constraints: Implementing all detection processes locally increases reliance on

client-side processing power and storage, potentially limiting the
extensiveness of detection algorithms.

o Finalization: Privacy-centered design was implemented without compromise,

with clear documentation for users on data handling policies. Constraints were
addressed by optimizing code to minimize local resource consumption.

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 2. Exclusions and Constraints Impacting Feature Decisions

Not all initially envisioned features were included, as certain constraints required careful
deliberation and feature reduction to meet practical and ethical requirements.

i. Machine Learning-Based Detection

o Rationale for Exclusion: While machine learning could improve pattern

recognition, it requires extensive resources, both in terms of computational
power and data collection, which could strain device performance and
compromise user privacy.

o Constraints Impact: Given the performance and privacy limitations, machine

learning was ruled out in favor of regex and DOM-based methods, which
provide sufficient accuracy for common dark patterns.

ii. Crowdsourced Dark Pattern Database

o Rationale for Exclusion: A shared database where users could report new dark
patterns was considered as a means to expand detection capabilities. However,
this feature would necessitate cloud storage and data sharing, raising privacy
and security concerns.

o Constraints Impact: The development team concluded that a local-only

detection model better aligns with privacy values, leaving the crowdsourced
database idea as a potential future feature contingent on secure and
anonymized data-handling solutions.

iii. Advanced Customization for Technical Users

o Rationale for Exclusion: Although providing a wide range of customization

options could enhance user experience for tech-savvy users, complex settings
could confuse less experienced users, making the tool less accessible.

o Constraints Impact: A simpler settings menu was chosen, allowing only

essential customizations (e.g., notification frequency) to balance simplicity
with user control.

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 3. Finalized Feature Set

Based on this analysis, the following core features were finalized for inclusion, as they deliver
effective dark pattern detection while respecting constraints around privacy, performance,
and user accessibility:

 Real-Time Detection: Enabled with optimized regex and DOM manipulation.

 Adjustable Notifications: Real-time alerts with optional customization.

 Visual Highlighting: Simple color overlays to pinpoint detected patterns.

 Local Processing for Privacy: Full detection functionality within the browser, no data
sharing.

4. Future Considerations and Constraints for Additional Features

Future updates to Hawk’s Detector may incorporate advanced features that were excluded in the
initial release, contingent upon overcoming privacy and performance constraints:

 AI-Based Detection: If feasible, anonymized local AI could be introduced to detect

more subtle patterns while preserving user privacy.

 Crowdsourced Reporting: Potentially allowing users to report dark patterns

anonymously, but only if secure data handling mechanisms are developed.

 Mobile Compatibility: Expanding to mobile browsers may require adapting the

interface and optimizing performance for mobile hardware limitations.

5. Scalability Considerations

i. Browser and Device Compatibility:

o Analysis: Ensuring Hawk’s Detector works smoothly across major browsers

(e.g., Chrome, Firefox, Edge) and operating systems is vital to reach a wide
audience.

o Constraints: Different browsers handle extensions and API support uniquely.

Developing a consistent experience across platforms increases development
complexity and testing time.

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 o Finalization: Compatibility with Chrome and Firefox was prioritized for the
initial release, as these are widely used. Future releases may add support for
other browsers and platforms, with adaptations made for each platform’s
unique APIs.

ii. Efficient Resource Utilization on Low-Power Devices:

o Analysis: Many users might run the extension on low-power devices, such as
laptops and tablets, where performance is crucial.

o Constraints: Running real-time detection with DOM manipulation can be

resource-intensive. This can cause delays on low-power devices, especially on
media-heavy or dynamically loaded websites.

o Finalization: The extension is optimized to perform in the background with

low resource consumption, minimizing memory and CPU usage. Additional
optimization techniques (e.g., lazy loading detection scripts) were considered
to reduce impact.

6. User Engagement and Feedback Mechanisms

i. Feedback System for User Reports:

o Analysis: Allowing users to report undetected patterns or provide feedback

can improve the extension and broaden detection capabilities.

o Constraints: Direct feedback collection may require external data storage, which
could raise privacy concerns and compliance issues.

o Finalization: A lightweight feedback form with optional reporting is included,

allowing users to submit feedback anonymously. This information can be
collected for future improvements while respecting user privacy.

ii. User Education and Awareness

o Analysis: Dark patterns are a complex subject, and educating users about their
nature helps raise awareness and reinforces the value of the extension.

o Constraints: Providing too much information could overwhelm users or clutter

the interface.

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 o Finalization: An accessible “Learn More” section was added, which provides
users with clear, concise information on types of dark patterns and tips on
spotting them. It also links to resources for users interested in deeper
knowledge.

7. Legal and Ethical Compliance

i. Privacy Regulations (GDPR, CCPA):

o Analysis: Compliance with privacy regulations is essential to ensure user data

security and to meet legal requirements in various regions.

o Constraints: To comply with GDPR, CCPA, and similar regulations, no

personal or browsing data can be transmitted or stored externally.

o Finalization: All processing is performed locally on the device to eliminate

any risk of non-compliance. Privacy assurances are clearly communicated to
users in the extension’s privacy policy and settings.

ii. Transparency and Consent:

o Analysis: Users need to understand how the extension operates, what data (if
any) it accesses, and how it affects their browsing experience.

o Constraints: Explaining these aspects without overloading users with technical

details can be challenging.

o Finalization: A simple, clear privacy policy is included, along with a

permissions request that explains what the extension does in user-friendly
language. An onboarding process highlights how Hawk’s Detector works and
reassures users about its safety and transparency.

8. Technical Limitations and Simplification Choices

i. DOM Manipulation vs. Advanced Algorithms:

o Analysis: DOM manipulation is effective for basic detection but may struggle
with highly dynamic content or advanced dark patterns.

o Constraints: Implementing machine learning or more advanced pattern

recognition was limited due to processing constraints and user privacy
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 considerations.

o Finalization: The initial release focuses on DOM manipulation and regex,

which are lightweight and sufficient for detecting common patterns. Advanced
algorithms may be reconsidered in future versions, potentially incorporating
anonymized local AI if feasible.

ii. Avoiding Over-Detection and False Positives:

o Analysis: Accurate detection without falsely flagging legitimate content is

critical to maintain trust.

o Constraints: High false positives would frustrate users, but avoiding them
entirely requires complex detection logic.

o Finalization: Detection thresholds were carefully calibrated through testing

across multiple sites. This includes allowing users to mark certain flags as
false positives, so the extension can "learn" and adapt its sensitivity.

9. User Accessibility and Inclusivity

i. Accessibility for Users with Disabilities:

o Analysis: Accessibility features such as screen reader compatibility, high-

contrast modes, and keyboard navigation ensure usability for users with visual
impairments or limited mobility.

o Constraints: Implementing these features required additional design and

testing resources to ensure compatibility with various assistive technologies.

o Finalization: Essential accessibility features were included, such as ARIA

labels, high-contrast options, and screen-reader-friendly descriptions, to ensure
that users of all abilities can benefit from the extension.

ii. Language and Localization:

o Analysis: Offering support in multiple languages could broaden the

extension’s accessibility and user base.

o Constraints: Translating content and providing support in multiple languages

would increase development time and complexity.
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 o Finalization: English was chosen as the default language for the initial release,
with the potential for multilingual support in future updates based on user
demand.

10. Future Expandability and Modular Design

i. Modular Architecture for Adding Features:

o Analysis: Developing Hawk’s Detector with modularity in mind allows for

easier feature additions and updates, ensuring the tool can evolve.

o Constraints: Developing a modular architecture requires more initial setup and

may increase code complexity.

o Finalization: The extension’s architecture was designed to be modular,

enabling easier updates and the addition of future features, such as machine
learning detection modules or community reporting features, without
extensive rework.

ii. Community and Open-Source Potential:

o Analysis: Open-sourcing the extension could allow the community to

contribute improvements, expanding detection capabilities through shared
insights.

o Constraints: Open-source projects require maintenance and may introduce

security risks if external code contributions aren’t thoroughly reviewed.

o Finalization: Open-source release is under consideration for the future. Initial

deployment will remain proprietary to control quality and security, but
releasing portions of the codebase could enable community input while
keeping core functionalities proprietary.

The final feature set of Hawk’s Detector was determined through a rigorous analysis of each
feature’s impact on the user experience and alignment with the project's core values of
privacy, transparency, and accessibility. By prioritizing ethical design and responsiveness to
user constraints, Hawk’s Detector effectively addresses the pervasive issue of dark patterns
while remaining user-friendly, compliant, and performant.

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3.4 Design Flow

Fig 3.4.1 Flowchart

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Fig 3.4.2 Process Flow

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3.5 Design Selection
The design selection process for Hawk’s Detector focused on building a browser extension
that effectively identifies and reports dark patterns while meeting critical constraints such as
privacy, performance, compatibility, and user experience. The following aspects were
considered during the design selection to ensure the best balance between functionality,
usability, and efficiency:

1. Technology Stack
The foundation of the extension is built using web technologies that are widely supported
across modern browsers. After evaluating several options, the following technologies were
selected:

JavaScript: Chosen for its flexibility and power in handling DOM manipulation and
interacting with the browser environment in real time. JavaScript is lightweight, well-
supported, and enables the extension to quickly respond to changes on web pages without
needing additional overhead.

HTML/CSS: Used for the user interface components to create a clean and intuitive design.
These standard technologies ensure that the extension’s visual elements are easily rendered
across different platforms without compatibility issues.

Browser Extension API: Selected for its cross-browser support, enabling the extension to
run on Chrome, Firefox, Edge, and other major browsers. The API provides the necessary
hooks to manipulate the DOM and respond to user actions effectively.

2. Real-Time Detection Approach

The design choice to use DOM manipulation and regular expressions (regex) for detecting
dark patterns was finalized after evaluating more complex alternatives, such as machine
learning models. The key factors influencing this selection were:

Efficiency: DOM manipulation allows the extension to directly access and analyze webpage
elements in real time. This is critical for real-time detection, as it enables instant
identification of dark patterns without introducing significant lag.
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Simplicity: Regular expressions provide a straightforward method for searching webpage
content for specific patterns of deception. This approach minimizes computational
complexity, making the extension faster and more responsive compared to machine learning
models.

Avoidance of Machine Learning Models: Although ML could provide more adaptability in

detecting evolving dark patterns, it would require data collection, external processing, and
more significant resource consumption. Given the constraints of privacy and performance,
DOM manipulation and regex are better suited to meet the needs of real-time detection while
keeping the design lightweight and privacy-conscious.

3. User Interface Design

The design of the user interface was focused on achieving clarity and ease of use for both
technical and non-technical users. The following design choices were made:

Minimalist Design: A simple, intuitive interface was selected to ensure users can easily
interact with the extension. Notifications and reports are presented in a clear, non-technical
manner, with visual cues like color highlights for detected dark patterns.

Real-Time Notifications: The decision was made to implement real-time notifications that
alert users immediately when a dark pattern is detected. These notifications are non-intrusive,
appearing discretely in the browser, and allow the user to take action without being
overwhelmed by pop-ups or warnings.

Transparent Reporting: Each dark pattern detected is accompanied by a clear explanation

of how it attempts to manipulate user behavior. This ensures that users are not only informed
but also educated about these deceptive practices, aligning with the project’s goal of
increasing digital literacy.

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 Fig. 3.5.1 User Interface Design

4. Cross-Browser Compatibility
To ensure a wider user base and adaptability, cross-browser compatibility was a major design
requirement. The extension needed to run seamlessly across different browsers while
maintaining consistent functionality and performance.

Standard APIs: The use of standard Web Extensions APIs allows the extension to run on
different browsers like Chrome, Firefox, and Edge without modification. This decision
ensures that the core functionality remains uniform across platforms, reducing the need for
browser- specific code adjustments.

Responsive Design: The interface is designed to be responsive, adjusting to various screen

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sizes and resolutions across different devices, from desktop to mobile browsers.

5. Performance Efficiency
Performance was a key concern, especially given the real-time nature of the extension. The
following design choices were made to ensure efficient operation:

Lightweight Processing: By avoiding resource-heavy processes, such as machine learning

models, the extension minimizes its impact on system performance. This is critical to
ensuring that the user’s browsing experience is not negatively affected by the tool’s
operation.

Event-Based DOM Manipulation: Instead of constantly scanning the webpage for dark
patterns, the extension triggers checks based on specific DOM events (e.g., when elements
are loaded or changed). This event-driven approach significantly reduces the amount of
processing required, conserving both memory and CPU usage.

The design selection for Hawk’s Detector was driven by the need to balance efficiency, user
privacy, cross-browser functionality, and real-time performance. By leveraging DOM
manipulation, regex, and standard web technologies, the extension offers a robust solution for
detecting dark patterns without compromising on usability or system performance. The final
design aligns with the project’s goals while adhering to the constraints, resulting in a tool that
is effective, accessible, and user-friendly.

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3.6 Implementation plan/methodology

A. Software Requirements

Since this project mainly focuses on browser extensions for various e-commerce websites, it
uses web development tools such as HTML, CSS, JavaScript, DOM, and technologies based
on Regex and Web Extension API. Existing databases should be stored and modified using
SQL. HTML, CSS, and JavaScript are considered front-end development tools.

1. HTML (Hyper-Text Markup Language)

HTML is used to structure the content of the browser extension's user interface. It defines
elements such as buttons, text, input fields, and layout that users interact with. In the case of
Hawk's Detector, HTML is crucial for creating the popup interface that displays warnings,
reports, and notifications when dark patterns are detected. It serves as the foundational
language for presenting the extension’s content.

2. CSS (Cascading Style Sheets)

CSS defines the appearance and structure of HTML elements, enhancing the user experience
by making extensions more visually appealing and easy to navigate. In Hawk’s Detector,
CSS will be used to style the warning messages and notifications that highlight detected dark
patterns, ensuring that the visual cues are clear and consistent across different browsers and
devices.

3. JavaScript

JavaScript is the core programming language used to control the logic and functionality of the
browser extension. It powers the real-time scanning of websites for dark patterns. In Hawk's
Detector, JavaScript enables the extension to interact with web pages, manipulate the
Document Object Model (DOM), and trigger alerts when manipulative design elements are
found. It also handles communication between the browser and the extension, ensuring
smooth operation.

4. DOM (Document Object Model)

The DOM is an interface that allows JavaScript to interact with the structure and content of a
webpage. In Hawk’s Detector, DOM manipulation is key to analyzing web page elements
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such

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as buttons, forms, and pop-ups to identify patterns that are deceptive or misleading. The
extension accesses and modifies the DOM in real time to detect dark patterns without altering
the underlying web page content.

5. Regex (Regular Expressions)

Regex is a powerful tool for pattern matching and searching specific sequences within text. In
the context of Hawk's Detector, regular expressions are used to detect phrases, terms, or
structures that are commonly associated with dark patterns, such as misleading labels or
forced consent mechanisms. Regex allows the extension to efficiently scan through text-
based elements on a webpage for these deceptive patterns.

6. Browser Extension Framework (Web Extensions API)

The Web Extensions API is a standardized interface used to develop extensions for multiple
browsers, such as Chrome, Firefox, and Edge. This API allows Hawk's Detector to function
seamlessly across various platforms by providing access to browser functionalities like tabs,
cookies, and page scripts. It also handles permissions and manages the background processes.

B. Flow of the Proposed Model

Our methodology for the Hawk's Detector browser extension follows a structured approach
aimed at developing a robust tool for detecting dark patterns.
The project is broken down into several phases, from initial research to deployment. Below is
a detailed plan of action:

1. User Navigates to Website

The user visits a website. The browser loads the webpage content, which includes all
interactive elements (buttons, forms, links, etc.

2. Content Extraction from DOM

The Hawk's Detector extension extracts the DOM structure of the webpage. This includes
HTML elements that are key to the user experience, such as pop-ups, buttons, and navigation
links. The extension captures these elements for further analysis without altering or
modifying the webpage content.

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3. Pattern Detection via JavaScript and Regex

The extension processes the DOM elements using JavaScript and Regex. It searches for
patterns that match dark design techniques, such as:

• Deceptive button labels.

• Hidden fees or terms.

• Pre-checked options.

• Forced actions (like signing up for newsletters automatically).

• And many more.

4. Comparison with Predefined Dark Pattern Rules

The extension compares the extracted elements against a set of rules that define dark patterns.
This rule set is maintained and updated to reflect common manipulative tactics used in web
design.
5. Dark Pattern Detection
If a match is found, the extension flags the element as a dark pattern.
If no match is found, the scanning process continues in the background as the user interacts
with the page.

6. User Alert and Report Generation

If a dark pattern is detected, the extension notifies the user through an on-screen pop-up or
notification. The notification contains details about the specific dark pattern and its potential
risks.

The user can access a full report that explains the nature of the dark pattern, offering
educational insights and guidance on avoiding it.

7. Local Processing and Privacy Protection

All analysis and detection are performed within the browser. No user data or browsing
history is sent to external servers, ensuring that privacy is maintained. The extension does not
store personal data or track user behavior.

C. Methodology

Our methodology for the Hawk's Detector browser extension follows a structured approach to
develop a robust tool for detecting dark patterns. The project is divided into several phases,
from initial research to deployment.

[64]
 Fig 3.6.1 Roadmap of the Browser Extension

Figure (3.6.1) presents a step-by-step guide detailing the development process of Hawk’s
Detector. it begins with research and requirements gathering, where we analyze dark patterns
and existing detection tools to define the extension's technical needs. Design and architecture
planning follows, focusing on a modular, cross-browser extension that maintains user privacy
through local data processing. During the development of detection algorithms,

JavaScript and Regex are utilized to identify dark patterns efficiently, with regular updates to
address new tactics. The user interface and notification system are crafted for simplicity,
providing real-time alerts and clear explanations of detected patterns. Comprehensive testing
and validation ensure the extension’s compatibility and accuracy across various different
browsers. Privacy is upheld by processing all data locally and adhering to regulations like
GDPR. Finally, deployment and maintenance involve distributing the extension, providing
user guides, and implementing ongoing updates based on feedback to enhance performance.

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Fig. 3.6.2 Working of Hawk’s Detector

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Fig. 3.6.3 Code of JS Functionality

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Fig. 3.6.4 Code of JS Functionality (Pattern Matching)

Fig. 3.6.5 Showing Functionality

[68]
 CHAPTER 4
RESULTS ANALYSIS AND VALIDATION

4.1 Implementation of solution

In the vast expanse of the digital landscape, user trust is paramount. However, the prevalence
of dark patterns — deceptive design strategies employed by websites — poses a significant
threat to online integrity. Enter "Hawk's Detector," an innovative project dedicated to
unveiling and neutralizing dark patterns embedded within websites. Hawk Detector is a
browser extension which works on different web browsers to scan websites for hidden traps
and manipulative tactics lurking behind the pixels.
Key Features of our extension:
1) Pattern Recognition: Pattern recognition refers to the ability to identify and understand
recurring design elements, features, or tactics used to manipulate users into making choices
they might not otherwise make. It's essentially about finding the "tricks of the trade"
employed in sneaky user interfaces.
2) Real-time analysis: Real-time analysis refers to the monitoring and evaluation of user
behavior and interaction with an interface, often coupled with algorithms to identify and
exploit vulnerabilities in real-time for manipulative purposes. This continuous tracking
allows dark pattern designers to adjust their tactics dynamically based on individual user
responses, making them even more deceptive and impactful.
3) Continuous Learning: Continuous learning refers to the systematic and ongoing
optimization of manipulative tactics based on real-time user data and feedback. This allows
designers of dark patterns to continuously refine their techniques, making them increasingly
persuasive and difficult to defend against.
4) User Friendly: A user-friendly is one that prioritizes the needs and experience of the user
above all else. It should be intuitive, helpful, and unobtrusive, seamlessly enhancing the
browsing experience without causing frustration or confusion.

Here are some key characteristics of a user-friendly web-browser extension:

• Clear Purpose and Value
• Simple and Intuitive Interface
• Customization and Control
• Consistent Design and Updates

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5) Risk Recommendation: Detecting and mitigating dark patterns involves understanding
potential risks associated with deceptive design practices and providing recommendations to
address those risks.
Here are some risk recommendations in terms of dark patterns:
• User Awareness
• User Feedback Mechanism
• Prompt Action on Violations
• User Customization

Here are some different types of Dark Patterns that our extension detects
1) Forced Continuity: Forced continuity is another manipulative pattern that stimulates the
user to start a free trial by submitting personal details such as card details. After that, the user
has no option to revert the process – the choice to skip the self-interest gimmick is neither an
option, and the user has no choice but to continue the registration to the end.
2) Confirm Shaming: Confirm Shaming attempts to leverage shame to motivate users to
take action. For example, many ecommerce websites use pop-ups where the opt-out option is
worded so that the user feels guilty or foolish if they don’t comply.
3) Disguised Ads: Disguised advertisement refers to the practice of presenting
advertisements in a manner that conceals their true nature, making them appear as other types
of content, such as user-generated content, news articles, or false advertisements. The
intention is to seamlessly integrate these ads into the overall interface, tricking customers into
clicking on them.
4) Hidden Cost: Hidden costs refer to intentionally concealing additional fees, charges, or
conditions during checkout. Users may only discover these costs after they have committed to
a purchase, leading to frustration and a sense of being deceived.
5) Misdirection: Misdirection involves redirecting the user’s attention away from important
information or options by using visual cues, color schemes, or misleading wording. This
tactic can trick users into making unintended choices or unknowingly consenting to
undesirable actions.
6) Social Proof: Social proof involves presenting users with fabricated or misleading
information to persuade them to take a desired action. This can include fake testimonials,
inflated user reviews, or artificial popularity indicators.
7) False Urgency: “False Urgency” refers to the deceptive practice of falsely conveying or
implying a sense of urgency or scarcity to mislead users into making immediate purchases or
taking prompt actions, potentially resulting in a purchase.

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 8) Scarcity: False scarcity creates a sense of urgency by falsely suggesting limited product
or service availability. Users may rush into purchasing, fearing they will miss out on the
opportunity, even if the scarcity is manufactured.

Results:

Fig. 4.1.1 Dark Pattern - Social Proof

Fig. 4.1.2 Dark Pattern - ConfirmShaming

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Fig. 4.1.3 Dark Pattern – Hidden Cost

Fig. 4.1.4 Dark Pattern – Scarcity

[72]
 CHAPTER 5
CONCLUSION AND FUTURE
5.1 Conclusion WORK

The development of Hawk's Detector marks a significant advancement in addressing the

widespread issue of dark patterns on online platforms, particularly within e-commerce. By
creating a browser extension that identifies dark patterns in real-time, the project offers a
proactive tool for consumers, allowing them to recognize and avoid manipulative design
tactics that aim to exploit user behaviour for commercial gain.
One of the primary achievements of Hawk’s Detector is its user-centric approach, prioritizing
accessibility and ease of use. By choosing not to incorporate machine learning algorithms, the
team ensured that the extension remains lightweight, fast, and efficient. This decision also
respects user privacy, as the tool operates without collecting or analysing personal data.
Instead, the extension employs DOM manipulation and regular expressions to analyse
webpage structures, enabling it to detect and highlight dark patterns without affecting user
privacy or adding computational overhead.
During development, critical factors such as cross-browser compatibility, performance
efficiency, and real-time notifications were carefully integrated. This focus ensures that
Hawk’s Detector functions smoothly across various platforms, reaching a broad audience
without compromising the browsing experience. The extension's compatibility with multiple
browsers and platforms further enhances its reach, making it an inclusive solution for diverse
user groups.
Impact on Consumer Rights and Digital Literacy: By helping users identify and understand
deceptive tactics, Hawk's Detector promotes greater transparency and digital literacy. It
empowers consumers to make informed choices and protects them from unethical design
strategies that could compromise their privacy, financial security, or autonomy. This aligns
with broader global efforts to uphold consumer rights and privacy in the digital domain,
supporting a fairer and more transparent internet landscape.
Moving forward, expanding Hawk’s Detector's capabilities offers exciting potential. Future
developments might include adding new types of dark pattern detection, refining the interface
to support even more languages and regions, and collaborating with other consumer
protection organizations to enhance its functionality. By continuing to innovate and respond
to evolving user needs, Hawk’s Detector has the potential to become a standard tool in
promoting ethical design practices and safeguarding online user rights.

[73]
In conclusion, Hawk's Detector provides a robust and accessible solution to combat dark
patterns, setting a foundation for future advancements in consumer protection tools. The
project demonstrates how technology can be effectively applied to promote ethical standards
and improve user experience, contributing to a safer and more transparent digital ecosystem.

Furthermore, Hawk's Detector stands as a model for how technology can be harnessed to
promote ethical digital practices and empower users in an increasingly complex online world.
By focusing on transparency, simplicity, and user privacy, the project addresses an urgent
need for tools that protect consumer rights without adding complexity to the user experience.
As the landscape of online deception evolves, Hawk's Detector has laid a groundwork that
can be expanded upon, both technically and in terms of reach. The project’s success
demonstrates the importance of innovation in consumer protection, offering a scalable
solution that can adapt to new forms of dark patterns as they emerge. Through continuous
development and potential partnerships with consumer advocacy groups, Hawk's Detector
can play a significant role in establishing safer online standards and fostering a culture of
digital responsibility.

The success of Hawk's Detector highlights the potential for scalable solutions that can evolve
to meet emerging challenges in the digital world. As new forms of dark patterns and
manipulative practices appear, Hawk’s Detector provides a flexible foundation for further
enhancements, such as integrating advanced detection techniques and collaborating with
other developers and organizations focused on digital ethics. Its emphasis on user education
is also pivotal, as empowering consumers to recognize these deceptive tactics contributes to
broader digital literacy. This project not only addresses an immediate need but also serves as
a catalyst for future innovation in consumer protection technology, underscoring the
importance of proactive solutions in the ongoing fight for a transparent and fair digital
environment.

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5.2 Future Work

While Hawk's Detector provides a robust solution to detecting dark patterns, there are several
areas for future improvement and expansion to ensure the project stays relevant as online
manipulation tactics evolve:

Enhanced Dark Pattern Detection:

Expand the library of detectable dark patterns by incorporating more complex behavior
patterns such as misleading pricing schemes, fake countdown timers, and hidden costs. This
could involve incorporating more sophisticated logic for detecting interaction-based patterns
that are harder to identify with simple DOM traversal and regex.
Implement AI-based analysis as an optional feature for users who are comfortable with
machine learning models. This could allow the extension to detect emerging dark patterns
based on user behavior data while maintaining a strict focus on user privacy.

Customizable User Experience:

Provide users with customization options, allowing them to set the sensitivity of dark pattern
detection based on their preferences. This feature could allow users to choose whether they
want to be alerted to minor manipulations or only more severe dark patterns.
Introduce personalized notifications that help users understand dark patterns relevant to their
specific browsing habits.

Collaborative Database for Dark Patterns:

Create a community-driven database where users can report new dark patterns they
encounter. This would allow for crowdsourced updates to the extension's detection library,
ensuring it remains effective against new manipulative tactics.
Develop a backend server that anonymously collects reports from users about dark patterns
and analyzes common trends to help detect new variations across the web.

Integration with E-Commerce Tools:

Integrate Hawk’s Detector with popular e-commerce plugins or payment gateways to give
users additional tools to fight dark patterns while shopping online. This could include alerting
users when they’re about to fall for a hidden subscription model or when deceptive pricing
tactics are used.
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Mobile Browser Compatibility:
Extend the functionality of the extension to mobile browsers, ensuring that users on mobile
devices are also protected from dark patterns. This would require optimizing the extension for
different screen sizes and ensuring compatibility with mobile versions of browsers like
Chrome and Firefox.

Educational Resources:
Incorporate educational resources directly into the extension, offering users explanations,
videos, or articles on how to identify and avoid dark patterns. This could help raise awareness
about digital rights and ethical design practices in a more engaging way.

Enterprise Adoption:
Explore potential integrations with enterprise-level security tools that companies can use to
ensure their own websites adhere to ethical design standards. This could help businesses
identify and avoid implementing dark patterns on their platforms, fostering better user
experiences.

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