“BRIDGING RISK AND INNOVATION: GENERATIVE AI”

A Mini Project report Submitted in partial fulfilment

of the requirement for the award of degree of
BACHELOR OF TECHNOLOGY
in

Computer Science & Engineering

(AI&ML)
Submitted By:
P.S.V.C.R ANUSHA (22N71A6637)
SRUJANA UPPULA (22N71A6643)
P.S.S.MANIKANTA (22N71A6655)
Under the Guidance of

Mrs.K. JYOTHI
(Assistant Professor, AI&ML Department)

Department of CSE-(AI&ML)
DRK INSTITUTE OF SCIENCE AND TECHNOLOGY
(Affiliated to JNT University, Hyderabad) Bowrampet, Hyderabad-500043
2024-2025
 Department of Computer Science &Engineering
(AI&ML)
DRK INSTITUTE OF SCIENCE AND TECHNOLOGY

(Affiliated to JNT University, Hyderabad) Bowrampet, Hyderabad-500043

CERTIFICATE
This is to certify that the project report entitled “BRIDGING RISK AND INNOVATION:
GENERATIVE AI” is being submitted by:

P.S.V.C.R ANUSHA (22N71A6637)

SRUJANA UPPULA (22N71A6643)
P.S.S.MANIKANTA (22N71A6655)
To JNTUH, Hyderabad, in partial fulfilment for the award of the Degree of Bachelor of
Technology in Computer Science and Engineering (AI&ML) to the Jawaharlal Nehru
Technological University is a record of bonafide work carried out by him under my guidance and
supervision. The results embodied in this project report have not been submitted to any other
University or Institute for the award of any Degree.

Signature of Guide Signature of HOD

Mrs. K. Jyothi Prof. (Dr). Durga Prasad Kavadi
Dept. of CSE (AI&ML) Dept. of CSE (AI&ML)

EXTERNAL EXAMINER
 ACKNOWLEDGEMENT

The satisfaction and euphoria that accompanies the successful completion of any task would be
incomplete without the mention of the people who made it possible and whose encouragement and
guidance helped me completing the project with success. We are highly indebted to Prof. (Dr).
Gnaneswara Rao Nitta, Principal of DRK Institute of Science and Technology, for immense
support during the tenure of the project.

We extend our deep sense of gratitude to Prof. (Dr). Durga Prasad Kavadi, Head of the
Department, Computer Science of Engineering (AI&ML), DRK Institute Of Science And
Technology, JNT University Hyderabad, for his support and guidance throughout our project
and for his valuable support and encouragement in completion of this project.

We are greatly indebted to Mrs. K. Jyothi, Assistant Professor, Department of Computer

Science of Engineering (AI&ML), for her insightful guidance and concern.

Finally, we express our gratitude to those who have helped us in successfully

Completing this project. Further, we would like to thank our family and friends for their moral
support and encouragement, and to all who have helped us in successfully completing this project.

P.S.V.C.R ANUSHA SRUJANA UPPULA

22N71A6637 22N71A6643
Dept. of CSE (AI/ML) Dept. of CSE (AI/ML)

P.S.S.MANIKANTA
22N71A6655
Dept. of CSE (AI/ML)
 ABSTRACT

Scenario creation is a vital process in strategic planning, helping organizations prepare for future
uncertainties. Traditionally, this process has been manual, expert-driven, and time consuming,
often limited by human bias and a lack of creative diversity. This project introduces a novel
approach using Generative Artificial Intelligence (Generative AI) to automate the generation of
realistic, diverse, and context-aware future scenarios. By leveraging pre-trained language models
such as GPT-2, the system accepts custom-designed prompts and produces human-like narrative
outputs. The generated scenarios are evaluated using linguistic metrics and human validation for
creativity, relevance, and coherence. The project also integrates ethical filtering to minimize bias,
misinformation, and hallucination in AI outputs. This AI-driven scenario generation framework
bridges the gap between traditional foresight methods and modern data-driven innovation. The
proposed system can be applied in various fields such as public policy, education, business
forecasting, and disaster management— offering a faster, scalable, and more intelligent way to
support strategic decisions
TABLE OF CONTEXT PAGE.NO
CONTENTS
1. INTRODUCTION 1
1.1 INTRODUCTION 1
1.2 PURPOSE 1
1.3 PROBLEM STATEMENT 1
2. SYSTEM SPECIFICATIONS 4
2.1 HARDWARE REQUIREMENT 4
2.2 SOFTWARE REQUIREMENTS 4
3. SOFTWARE AND HARDWARE SPECIFICATIONS 5
3.1 REQUIREMENT ANALYSIS 5
3.2 REQUIREMENT SPECIFICATIONS 5
3.2.1 FUNCTIONAL REQUIREMENTS 5
3.2.2 SOFTWARE REQUIREMENTS 5
3.2.3 HARDWARE REQUIREMENTS 5
4. LITERATURE SURVEY 10
5. SYSTEM ANALYSIS 12
5.1 EXISTING SYSTEM 12
5.2 PROPOSED SYSTEM 13
6. MODULES 15
6.1 MODULES 15
6.2 MODULES DESCRIPTION 16
7. SYSTEM DESIGN 18
7.1 SYSTEM ARCHITECTURE 18
7.2 APPLICATION DESIGN AND SEMANTIC CHUNKING FLOW 19
7.3 CLUSTER -BASED SCENARIO PROJECTION DESIGN 20
 7.4 DATA FLOW DIAGRAM 21
7.5 USECASE DIAGRAM 22
7.6 CLASS DIAGRAM 23
7.7 ACTIVITY DIAGRAM 24
8. SOURCE CODE 39
9. SYSTEM STUDY 41
9.1 FEASIBILITY STUDY 41
9.1.1 TECHNICAL FEASIBILITY 42
9.1.2 ECONOMICAL FEASIBILITY 42
9.1.3 OPERATIONAL FEASIBILITY 42
10. SYSTEM TESTING 43
10.1 SYSTEM TESTING 44
10.1.1 UNIT TESTING 44
10.1.2 INTEGRATION TESTING 44
10.1.3 FUNCTIONAL TESTING 44
10.2 SYSTEM TESTING 45
10.2.1 WHITE BOX TESTING 45
10.2.2 BLACK BOX TESTING 46
11. OUTPUT SCREENS 48
11.1 OUTPUT 48
11.2 OUTPUT 49
11.3 OUTPUT 50
12. CONCLUSION 52
13. FURTHER ENHANCEMENTS 53
14. REFERENCES 54
CHAPTER -1
 INTRODUCTION

1.1 Introduction
In recent years, Artificial Intelligence (AI) has played a vital role in transforming industries
through intelligent automation and decision-making. One of the most powerful advancements in
this domain is Generative AI, which focuses on producing new content such as text, images, audio,
or video by learning from large datasets. Unlike traditional AI models that only analyze or classify
information, generative models like GPT (Generative Pre-trained Transformer) are capable of
generating human-like outputs based on a given prompt.
This mini project explores the application of Generative AI in the area of scenario creation, a
technique used by industries and governments to plan for possible future events. Instead of relying
on manual input from experts, we utilize AI to automatically generate multiple, creative, and
realistic future scenarios, making the planning process more efficient, faster, and adaptable.
1.2 Purpose
The purpose of this project is to implement a Generative AI-based system that can automatically
generate diverse, relevant, and creative future scenarios. By using pre-trained models like GPT-2,
the project aims to support decision-makers, researchers, and planners with quick, AI-generated
insights. This reduces dependency on manual effort and helps generate multiple perspectives for
future planning. In addition, the project promotes responsible AI usage by ensuring outputs are
ethically filtered and free from harmful or biased content. It serves as a step toward modernizing
the traditional scenario-building process through intelligent automation
1.3 Problem Statement
In today’s rapidly changing world, organizations and policymakers must anticipate future
possibilities to make informed decisions. Scenario creation plays a key role in this process, helping
stakeholders plan for uncertainties and assess potential risks. However, traditional scenario
generation methods rely heavily on human expertise, manual research, and historical data analysis.
These methods are not only time-consuming but also prone to human bias, lack of diversity, and
limited scalability. As the complexity of global challenges increases, there is a growing need for
intelligent systems that can produce diverse, creative, and realistic scenarios efficiently. This
project addresses that gap by developing a system based on Generative AI that can automatically
generate future scenarios using pre-trained models like GPT. The goal is to enhance the scenario
planning process by making it faster, more adaptable, and less dependent on manual effort—while
also ensuring the generated outputs are ethical, coherent, and contextually relevant

BRIDING RISK AND INNOVATION IN GENERATIVE AI 1

 CHAPTER -2

BRIDING RISK AND INNOVATION IN GENERATIVE AI 2

 SYSTEM SPECIFICATIONS

2.1 Hardware Requirements

 Processor: Intel Core i5 or above
 RAM: Minimum 8 GB
 Hard Disk: 10 GB of free space
 GPU (optional): NVIDIA CUDA-enabled for faster processing
 Monitor: Standard resolution (1024×768 or higher)
2.2 Software Requirements
 Operating System: Windows 10 / Ubuntu / macOS
 Programming Language: Python 3.8 or higher
 Development Tools: Jupyter Notebook / Google Colab
Libraries/Frameworks:
 Transformers (HuggingFace)
 NumPy
 Pandas
 Scikit-learn
 Matplotlib / Seaborn
 TensorFlow or PyTorch

BRIDING RISK AND INNOVATION IN GENERATIVE AI 3

 CHAPTER -3

BRIDING RISK AND INNOVATION IN GENERATIVE AI 4

SOFTWARE AND HARDWARE SPECIFICATIONS
3.1 REQUIREMENT ANALYSIS
The proposed system aims to automate the scenario creation process using Generative AI to
enhance strategic planning and decision-making. The system needs to be user-friendly, scalable,
and compatible with common platforms such as Google Colab or Jupyter Notebook. It is essential
to ensure minimal setup time and support for widely used AI development tools and frameworks.
The model must also be able to generate reliable outputs and handle a variety of input prompts.
3.2 REQUIREMENT SPECIFICATION
3.2.1 Functional Requirements
 User-friendly interface to input prompts and view generated scenarios
 Scenario output display in structured format
 Option for content filtering and refinement
3.2.2 Software Requirements
For developing the application, the following are the software requirements:
 Programming Language: Python 3.8+
 Operating System: Windows 10 / Linux / macOS
 Tools: Google Colab / Jupyter Notebook
 Libraries: Transformers, NumPy, Pandas, Scikit-learn
 Debugger: Any modern browser (preferably Google Chrome)
3.2.3 Hardware Requirements
For developing and testing the application, the following hardware is required:
 Processor: Intel i5 or above
 RAM: Minimum 8 GB
 Hard Disk: At least 20 GB free space
 GPU: Optional (recommended for faster processing)

BRIDING RISK AND INNOVATION IN GENERATIVE AI 5

 CHAPTER-4

BRIDING RISK AND INNOVATION IN GENERATIVE AI 6

 LITERATURE SURVEY
1. The Futures Triangle and Scenario Building – Inayatullah (2008) This foundational paper
introduced the concept of the Futures Triangle, a model used in futures studies to understand how
the past, present, and future influence one another. The triangle consists of three forces: the “pull
of the future” (visions and goals), the “push of the present” (trends and drivers), and the “weight
of the past” (barriers and legacy systems). This framework is often used to map out strategic
possibilities and was highly relevant for our project, as it helped frame the idea of generating future
scenarios that consider both current trends and past influences. The use of such a structured visual
model inspired the prompt design and content logic of our Generative AI outputs.

GENERATIVE AI SCENARIO CREATION

BRIDING RISK AND INNOVATION IN GENERATIVE AI 7

2. Scenario Planning: A Tool for Navigating Strategic Risks – Daheim & Uerz (2006)
This work focused on the need for structured, forward-thinking scenario creation techniques in
environments filled with uncertainty. The authors proposed a systematic process for constructing
scenario narratives to manage long-term risks. Their emphasis was on creating multiple,
contrasting future outcomes to prepare decision-makers for various possibilities. This helped our
project understand how human-driven scenario planning works and the importance of narrative
diversity. We adapted this multi- scenario idea in our project by generating different outputs per
prompt to simulate alternative futures.

3. A Classification of Scenario Planning Techniques – Amer, Daim & Jetter (2013)

Amer et al. presented a framework to categorize existing scenario planning methods based on how
exploratory or normative they are, the data they use, and how many variables they consider. This
paper gave us a solid understanding of the planning landscape and helped us define the kind of
scenarios our system should produce. Instead of relying on purely exploratory or data-heavy
methods, we aimed to balance creativity and coherence using generative models. This paper guided
our evaluation strategy in assessing whether the AI-generated scenarios aligned with real-world
strategic planning goals.

AI RESPONSE

BRIDING RISK AND INNOVATION IN GENERATIVE AI 8

4. GPT-3: What’s it Good For? – Floridi & Chiriatti (2020)
This research critically evaluated GPT-3, one of the most advanced language models developed
by OpenAI. While the paper acknowledged GPT-3’s remarkable ability to generate coherent and
context-aware text, it also warned about its philosophical limitations —such as lack of true
understanding and risk of misinformation (AI hallucinations). These insights were crucial to our
project. While we used GPT-2, the challenges noted in this paper made us more aware of the
ethical risks and the need to include filters and evaluation mechanisms to check the quality and
appropriateness of generated content.

PRINICIPLES OF CORE AI ETHICS

5. Ethics of Algorithms: Mapping the Debate – Mittelstadt et al. (2016)

This paper explored the ethical implications of algorithmic decision-making. It highlighted
common concerns such as fairness, accountability, transparency, and the need for oversight when
deploying intelligent systems. For our project, this work reinforced the idea that Generative AI is
not just a technical tool—it also comes with responsibility. We addressed this by adding post-
processing filters and scenario relevance scoring to ensure the system generated outputs aligned
with human values and ethical considerations.
6. AI and the Future of Growth – Bughin et al. (2019)
The study analyzed how AI technologies, including generative systems, would shape global
economic growth and innovation. It forecasted that AI could add trillions of dollars to the global
economy but also warned about job displacement and inequality. This paper helped us frame the
potential societal impacts of our project. The ability to create multiple AI-generated future
scenarios could assist policymakers and organizations in proactively addressing future challenges,
particularly in areas such as employment trends, education systems, and health infrastructure.
7. Governance of AI: A Call for International Regulation – Truby (2020)

BRIDING RISK AND INNOVATION IN GENERATIVE AI 9

Truby argued for the urgent need for coordinated, international legal frameworks to manage the
growing influence of AI technologies. He emphasized that without proper regulation, AI could be
misused, leading to legal and ethical issues. For our project, this paper highlighted the importance
of responsible AI development. We took inspiration from it by ensuring our system does not
generate harmful or misleading content and by limiting outputs to safe, informative, and strategic
narratives only.

AI-SUPPORTED FORESIGHT
8. The Global Landscape of AI Ethics Guidelines – Jobin, Ienca & Vayena (2019)
This research paper conducted a global review of over 80 AI ethics guidelines issued by
governments, academic institutions, and companies. It identified key recurring principles such as
transparency, accountability, fairness, human dignity, and safety. These principles were used in
our project to shape how the model generates and presents information. The paper served as a
benchmark for building ethical safeguards into the system design.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 10

 CHAPTER -5

BRIDING RISK AND INNOVATION IN GENERATIVE AI 11

 SYSTEM ANALYSIS

5.1 EXISTING SYSTEM

The existing scenario planning systems are predominantly manual and depend heavily on expert
knowledge, historical data, and subjective interpretation. These methods involve brainstorming
sessions, trend analysis, and qualitative forecasting, typically done by human analysts or domain
experts. While effective in traditional strategic settings, these systems are limited in terms of
creativity, speed, and adaptability to new or evolving information.
The traditional approach usually produces a limited number of scenarios due to time constraints
and the cognitive limitations of human forecasters. Additionally, since human judgment plays a
major role, there is a high possibility of incorporating personal bias, overlooking unlikely but
impactful possibilities, and repeating historical assumptions without challenging them.
DISADVANTAGES OF EXISTING SYSTEM
 Time-Consuming: Requires extensive manual research, brainstorming, and analysis.
 Limited Creativity: Human experts often generate predictable or narrow scenario sets.
 Prone to Bias: Subjective interpretations can lead to biased and less diverse outcomes.
 Low Scalability: Creating multiple scenario versions for different domains or inputs is not
feasible manually.
 Lack of Real-Time Adaptability: Cannot respond quickly to new trends or dynamic inputs.

5.2 PROPOSED SYSTEM

The proposed system is designed to generate future scenarios using a Generative AI model,
specifically the GPT-2 architecture. Instead of depending on manual analysis or expert-written
narratives, this system takes user input in the form of prompts and produces multiple context-aware
scenario outputs. It aims to enhance strategic decision-making through automated, creative, and
diverse scenario generation. The system includes pre-processing steps, safe prompt design, and
evaluation filters to ensure quality and ethical content output.
The model is hosted on platforms like Google Colab or Jupyter Notebook and utilizes pre-trained
language models to create rich, meaningful text. The generated scenarios can be applied across
domains such as healthcare, education, disaster management, and policy planning. The system is
designed to be lightweight, user- friendly, and capable of generating results in real-time.
ADVANTAGES OF PROPOSED SYSTEM
 The system produces multiple scenario outputs for a single input prompt.
 It reduces human effort and speeds up the scenario generation process.
 The model supports a wide range of topics and can be used across domains.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 12

  It reduces manual bias in scenario creation.
 The system can be updated or fine-tuned with minimal technical effort.
 Ethical filtering ensures safe and appropriate content.
 Easy deployment using free tools like Google Colab.

ALGORITHMS USED
The core of the system is based on the Generative Pre-trained Transformer (GPT-2) model.
The following are the algorithms and techniques used:
Transformer Architecture: A self-attention-based deep learning model used to handle long-
range dependencies in text.
Tokenization (BPE): Breaks down text into subword units to handle rare or unknown words.
Text Generation (Top-k / Top-p Sampling): These sampling techniques ensure diversity and
coherence in generated text by selecting next-word candidates from a probability distribution.
Prompt-to-Response Modeling: Uses a one-directional transformer to predict the next word
based on previous input tokens, enabling smooth, fluent text generation.
These components together allow the model to generate human-like, creative, and relevant
content for future scenario simulation.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 13

 CHAPTER-6

BRIDING RISK AND INNOVATION IN GENERATIVE AI 14

 MODULES
IMPLEMENTATION
6.1 MODULES
The proposed Generative AI system for scenario creation is divided into the following key
functional modules:
 Prompt Input Module
 Text Pre-processing Module
 Scenario Generation Module
 Content Filtering Module
 Scenario Evaluation Module
 User Output Display Module

6.2 MODULE DESCRIPTION

Prompt Input Module
This is the first and most essential module of the system, acting as the main interaction point
between the user and the application. In this module, users are prompted to enter a scenario-related
input or query, which forms the base of what the system will generate. These prompts can be open-
ended questions, domain-specific topics, or thematic ideas such as “What will education look like
in 2050?” or “How will healthcare evolve after AI integration?” The module ensures proper input
validation by checking if the entered prompt is not empty, too short, or irrelevant. The input is
stored and passed securely to the subsequent modules for processing. A smooth and intuitive
interface for prompt submission is crucial, as it directly influences the relevance and quality of the
generated output.
Text Preprocessing Module
After receiving the input from the user, it must be converted into a format that the Generative AI
model can understand. This is done through the Text Preprocessing Module. Here, the text
undergoes several cleaning operations such as removal of punctuation, extra spaces, and special
characters. It also converts all words to lowercase to maintain uniformity. One of the most critical
steps in this module is Tokenization, where the input sentence is broken into smaller units (tokens).
The GPT-2 model uses Byte Pair Encoding (BPE) for tokenization, which splits text into subword
units. This technique helps the model handle rare or unknown words effectively and improves its
ability to generalize. The final output of this module is a structured, clean sequence of tokens ready
to be sent to the language model for content generation.
Scenario Generation Module

BRIDING RISK AND INNOVATION IN GENERATIVE AI 15

This is the heart of the system. The Scenario Generation Module takes the tokenized prompt and
feeds it into a pre-trained GPT-2 model hosted on platforms like Google Colab or integrated via
HuggingFace’s API. GPT-2 uses the Transformer architecture with self attention mechanisms to
predict the next word in a sentence, generating fluent and meaningful text. The system uses
sampling techniques like Top-k and Top-p (nucleus sampling) to add diversity and control to the
text generation process. These techniques prevent the model from being repetitive or too generic
by randomly choosing the next word from a set of likely candidates. This module is capable of
generating one or more responses for the same prompt, offering users a variety of scenarios to
analyze or select from. The generated content is human-like, creative, and contextually relevant,
making it suitable for forecasting and decision support tasks.
Content Filtering Module
Not all AI-generated content is suitable for public or strategic use. This module ensures the outputs
are ethical, relevant, and safe. The Content Filtering Module checks for offensive language, biased
statements, hallucinations (false content), and irrelevant information. It uses predefined rules,
keyword blacklists, and sentiment analysis tools to flag or remove inappropriate content. This
filtering is essential in aligning the system with ethical AI standards, especially when scenarios
are being used for sensitive domains like healthcare, education, or public policy. The content that
passes this stage is considered reliable and is forwarded for evaluation.
Scenario Evaluation Module
The quality of AI-generated content must be measured to ensure its usefulness. The Scenario
Evaluation Module analyzes the outputs based on several criteria, including relevance, fluency,
grammar, and contextual accuracy. Common evaluation metrics include:
 BLEU Score – to compare the similarity of generated content with reference examples
 Perplexity – to assess the fluency and predictability of the language
 Relevance Scoring – using cosine similarity or contextual embeddings The best scenarios
are ranked and retained. This module may also support user feedback integration, where
users can rate scenarios, helping the system learn and improve future performance.
User Output Display Module
This is the final module where the user receives the result of their input in a clear and user-friendly
format. The selected or highest-rated scenario is displayed in a text box, table, or visual block.
Users are also given options to regenerate the scenario, copy or export the text, or analyze multiple
responses. The display is designed to be clean and readable, allowing users from both technical
and non-technical backgrounds to interact with the system. If multiple scenarios are generated,
they are shown in order of relevance, offering the user diverse perspectives on the same prompt.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 16

 CHAPTER -7

BRIDING RISK AND INNOVATION IN GENERATIVE AI 17

 SYSTEM DESIGN

7.1 SYSTEM ARCHITECTURE

The proposed system architecture for Generative AI-based Scenario Creation is modular,
consisting of sequential layers that perform input handling, processing, content generation, and
output delivery. The process begins with the user interface, where the user inputs a prompt or
question. This is passed to the Preprocessing Layer, which performs cleaning and tokenization of
the input using Byte-Pair Encoding (BPE).

The preprocessed text is then sent to the Generative Model Layer, where the GPT-2 model
processes the input prompt using the Transformer architecture. The model generates output text
based on contextual understanding and pre-learned patterns. This generated output is passed
through the Content Filtering Layer, which checks for ethical and safety compliance, and removes
any biased or inappropriate content.
Following filtering, the Evaluation Layer measures the quality of the output using metrics such as
Perplexity and Relevance Score. Finally, the Display Layer presents the best scenario(s) to the user
in a structured and readable format.
This architecture ensures modularity, ethical content generation, adaptability across domains, and
ease of integration with platforms like Google Colab or web-based interfaces.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 18

7.2 APPLICATION DESIGN AND SEMANTIC CHUNKING FLOW
The diagram below represents the application design architecture for a Generative AI-based
scenario generation system. It is divided into two parts:
(a) System Pipeline Overview The first part (on the left) shows the high-level system flow.
The process begins with a Risk Topic, which is used to construct dynamic queries via a
Query Builder. These queries are applied across multiple information sources to retrieve
relevant text content. The collected texts form a Text Corpus, which serves as the input for
the data analytics layer. The data analytics process involves semantic chunking of the news
content, followed by event extraction and clustering. Key sub-processes like Derivation,
Duplicate Removal, and Relevance Classification help in refining and narrowing down the
most influential events, which are later use in scenario modeling.

RISK TOPIC AND ANALYSIS THROUGH CHARTS

(b) Semantic Chunking Visualization The second part (on the right) illustrates the semantic
chunking process using cosine distance analysis. It shows how the sentences in a news
article are divided into semantically distinct chunks based on their contextual similarity.
The X-axis represents sentence position, while the Y-axis shows cosine similarity values.
Different colored zones represent chunks (Chunk #0, Chunk #1, Chunk #2), and thresholds
(red and green lines) define the boundaries for sentence and chunk similarity. This method
ensures that only thematically consistent groups of sentences are passed to the scenario
generation module.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 19

7.3 CLUSTER -BASED SCENARIO PROJECTION DESIGN
The following diagram demonstrates the process of projecting future scenarios using clusters
derived from real-time data. In this architecture, the input data (such as news reports, events, and
political decisions) is first grouped into influence clusters based on common themes or driving
factors. These clusters act as descriptors that define the current state of a particular risk topic. For
example, one cluster represents political opposition to combustion engine bans, while another
reflects environmental resistance.
Each cluster contains multiple events or developments that influence decision-making. The system
then applies Generative AI techniques, specifically prompting with large language models
(LLMs), to generate possible projections—which are the expected future outcomes based on each
cluster. A consistency matrix is used to assess and rank the logical coherence of these projections,
ensuring that the final scenario combinations are both diverse and realistic.
This model supports multi-scenario planning by considering several combinations and selecting
the most internally consistent projections. It enhances the creativity and coverage of strategic
planning processes by automating scenario logic through AI.

CLUSTERING

BRIDING RISK AND INNOVATION IN GENERATIVE AI 20

7.4 DATA FLOW DIAGRAM
The Data Flow Diagram (DFD) provides a visual representation of how data moves through the
system. It helps in understanding the logical flow of information from the user's input to the final
output. In this Generative AI-based scenario creation system, the DFD outlines the various
processes involved—such as input handling, preprocessing, generation, filtering, evaluation, and
output delivery.
The diagram identifies key entities (like the user), data stores (e.g., scenario repository), and
system processes (like GPT-based generation and content filtering). The goal of this diagram is to
clearly explain how raw input (a prompt) is transformed into a refined, meaningful output (a
scenario) with the help of modular processing stages.

DATA FLOW DIAGRAM OF VISUAL REPRESENTATION OF DATA

BRIDING RISK AND INNOVATION IN GENERATIVE AI 21

7.5 USECASE DIAGRAM
A Use Case Diagram provides a high‐level, graphical overview of how external actors (e.g., “End
User,” “Administrator”) interact with your system’s major functions (e.g., “Upload Prompt,”
“Generate Scenario,” “Review Output”). It doesn’t show internal logic or data flows—instead it
illustrates who can do what and what services the system offers. Each oval (“use case”) represents
a distinct user‐visible action or goal, while stick figures represent actors that initiate those actions.
By laying out all the primary interactions in one place, a Use Case Diagram makes it easy to:
 Capture and validate functional requirements with stakeholders
 Ensure that every user role has the necessary system support
 Identify missing functionality or unnecessary features early on
 Serve as a foundation for writing more detailed requirements and test cases
This simple “actor-to-use-case” view helps both technical and non‐technical team members align
on the system’s scope and intended behavior.

USE CASE DIAGRAM FOR BRIDGING RISK

BRIDING RISK AND INNOVATION IN GENERATIVE AI 22

7.6 CLASS DIAGRAM
The class diagram models a pipeline where a central Controller coordinates the flow: user input
arrives at the PromptHandler, which validates and logs it in PromptLog before passing it to the
Preprocessor. The Preprocessor tokenizes and embeds the prompt, handing it off to the
ScenarioGenerator, which invokes the language model to produce draft scenarios.

CLASS DIAGRAM FOR BRIDGING RISK

BRIDING RISK AND INNOVATION IN GENERATIVE AI 23

These drafts are vetted by the ContentFilter to remove any off-topic or unsafe content, then scored
and ranked by the EvaluationEngine, which also archives all variants in ScenarioRepository.
Finally, the OutputRenderer fetches the top-ranked scenarios and formats them for the user. Each
class has a single, well-defined role—input handling, data transformation, generation, filtering,
evaluation, or rendering—and the two data stores ensure traceability (PromptLog) and
reproducibility (ScenarioRepository). This separation of concerns makes the system both
maintainable and extensible.
7.7 ACTIVITY DIAGRAM
The activity diagram for the scenario‐generation workflow starts when the User submits a prompt.
First, the PromptHandler validates and logs the input. Next, control flows to the Preprocessor,
which tokenizes and embeds the prompt. The cleaned inputs are then passed to the
ScenarioGenerator, where the language model produces raw scenario drafts. Those drafts move to
the ContentFilter—any unsafe or irrelevant text is culled. Validated drafts proceed to the
EvaluationEngine, which scores and ranks them before saving everything in the
ScenarioRepository. Finally, the OutputRenderer retrieves the top results and formats them for
display back to the User, completing the process. This stepwise flow ensures each decision and
transformation is clearly traced and managed.

ACTIVITY DIAGRAM FOR BRIDGING RISK

BRIDING RISK AND INNOVATION IN GENERATIVE AI 24

 CHAPTER -8

BRIDING RISK AND INNOVATION IN GENERATIVE AI 25

 SOURCE CODE

import random
import ipywidgets as widgets
from IPython.display import display, Markdown

event_timeline = {
"technology": {
"past": [
"The internet became widespread in the 1990s.",
"Smartphones revolutionized life in the 2010s.",
"AI tools like Siri and Alexa emerged in the 2010s."
],
"present": [
"Generative AI is transforming industries in 2025.",
"5G and IoT are powering smart homes and cities.",
"AI is integrated into education, health, and finance."
],
"future": [
"AI will surpass human intelligence in specific fields by 2030.",
"Quantum computers will become commercially available in 2028.",
"Personal AI assistants will be as common as smartphones by 2027."
]
},
"health": {
BRIDING RISK AND INNOVATION IN GENERATIVE AI 26
 "past": [
"Vaccines eradicated smallpox in the 1970s.",
"MRIs and robotic surgeries became common by 2000.",
"COVID-19 shaped global healthcare systems in 2020."
],
"present": [
"CRISPR is being used for genetic editing.",
"AI is diagnosing diseases faster than doctors.",
"Wearables track vital signs in real time."
],
"future": [
"Cancer may become fully treatable by 2035.",
"CRISPR will eliminate many hereditary diseases by 2030.",
"AI mental health support will be used by 60% of teens in 2026."
]
},
"education": {
"past": [
"Traditional chalkboard teaching dominated till the 1990s.",
"E-learning rose with the internet in the 2000s.",
"Massive Open Online Courses (MOOCs) launched in 2010s."
],
"present": [
"AI tutors assist students in real-time.",
"Blended learning is used in schools worldwide.",
"Students use VR headsets in virtual classrooms."
BRIDING RISK AND INNOVATION IN GENERATIVE AI 27
 ],
"future": [
"VR classrooms will replace traditional learning by 2030.",
"AI tutors will be used in 90% of schools by 2027.",
"Degrees may be replaced by AI-certified portfolios by 2029."
]
},
"sports": {
"past": [
"India won its first Cricket World Cup in 1983.",
"Football became the world’s most watched sport by 2000.",
"Olympic sports saw technological doping concerns rise."
],
"present": [
"AI is used for player performance and injury prevention.",
"Esports have become globally recognized competitions.",
"Wearables optimize athlete training and recovery."
],
"future": [
"India will win the Cricket World Cup again in 2027.",
"Esports will be part of the Olympics by 2032.",
"Robot football teams may beat human players by 2040."
]
},
"politics": {
"past": [
BRIDING RISK AND INNOVATION IN GENERATIVE AI 28
 "UN was formed after World War II in 1945.",
"Cold War shaped international politics till 1991.",
"Arab Spring uprisings changed Middle East in 2010s."
],
"present": [
"Global AI regulation debates are ongoing.",
"Climate change dominates political platforms.",
"Digital voting and e-governance are growing."
],
"future": [
"Global AI governance laws will be signed by 2028.",
"A political party run by AI may emerge by 2033.",
"Climate change policies will decide elections by 2030."
]
},
"environment": {
"past": [
"Paris Agreement was signed in 2015.",
"Ozone recovery started after banning CFCs.",
"Industrialization led to deforestation worldwide."
],
"present": [
"2025 sees record heatwaves and wildfires.",
"Plastic bans and solar incentives are trending.",
"Global carbon levels are monitored by AI satellites."
],
BRIDING RISK AND INNOVATION IN GENERATIVE AI 29
 "future": [
"80% of the world will run on renewables by 2035.",
"AI-powered artificial trees will clean air by 2030.",
"Climate refugees will reshape migration by 2040."
]
},
"transportation": {
"past": [
"Steam engines transformed mobility in the 1800s.",
"Jet travel became globalized in the 1950s.",
"Electric cars entered mainstream in the 2010s."
],
"present": [
"EVs and hybrid vehicles dominate auto markets.",
"Hyperloop and autonomous taxis are in testing.",
"Air taxis and drone deliveries are being piloted."
],
"future": [
"Flying cars will be in cities by 2035.",
"Space travel for civilians will be affordable by 2040.",
"AI traffic control will reduce congestion to near zero."
]
},
"finance": {
"past": [
"The Great Depression shook global markets in the 1930s.",
BRIDING RISK AND INNOVATION IN GENERATIVE AI 30
 "Bitcoin launched as decentralized currency in 2009.",
"ATMs and online banking reshaped finance in 2000s."
],
"present": [
"AI handles risk and fraud detection in banks.",
"Digital wallets and UPI dominate payments in 2025.",
"Crypto is semi-regulated in most countries."
],
"future": [
"CBDCs will replace physical currency by 2030.",
"AI advisors will replace human financial agents by 2032.",
"DeFi platforms will dominate global finance by 2040."
]
},
"space": {
"past": [
"Apollo 11 landed humans on the Moon in 1969.",
"Hubble Telescope launched in 1990 transformed astronomy.",
"Mars rovers like Spirit and Curiosity brought new data."
],
"present": [
"NASA and SpaceX aim for Mars missions by 2030.",
"India's Chandrayaan-3 succeeded in 2023.",
"James Webb explores distant exoplanets in 2025."
],
"future": [
BRIDING RISK AND INNOVATION IN GENERATIVE AI 31
 "Humans will land on Mars by 2035.",
"Lunar habitats will be built by 2032.",
"Asteroid mining will begin commercially by 2040."
]
},
"cybersecurity": {
"past": [
"The 'ILOVEYOU' virus caused global damage in 2000.",
"Cybercrime increased after 2010 with digital banking.",
"Governments formed cyber armies in the 2010s."
],
"present": [
"AI is used to detect cyber threats in real-time.",
"Biometric authentication is standard.",
"Hacktivism and ransomware are major risks."
],
"future": [
"Quantum-proof encryption will be standard by 2030.",
"Brain-computer hacks will be a real threat by 2040.",
"AI cyberwarfare between nations will be a major concern."
]
},
"entertainment": {
"past": [
"Movies transitioned from silent to sound in the 1920s.",
"Color TV became mainstream in the 1960s.",
BRIDING RISK AND INNOVATION IN GENERATIVE AI 32
 "Streaming disrupted cinema in the 2010s."
],
"present": [
"AI is generating music, scripts, and visuals.",
"Virtual influencers and AI actors are trending.",
"Interactive content dominates platforms in 2025."
],
"future": [
"AI-generated films will premiere by 2030.",
"Holographic concerts will replace live shows.",
"Brain-connected entertainment will rise by 2040."
]
},
"agriculture": {
"past": [
"The Green Revolution boosted crop yields in 1960s.",
"Mechanization reduced manual farming labor.",
"GM crops improved resistance to disease."
],
"present": [
"AI drones monitor crops in real-time.",
"Urban vertical farms grow food in cities.",
"Lab-grown meat is becoming viable."
],
"future": [
"Climate-proof crops will be common by 2035.",
BRIDING RISK AND INNOVATION IN GENERATIVE AI 33
 "Fully automated robotic farms will dominate by 2030.",
"Synthetic foods will become the norm by 2040."
]
},
"urban": {
"past": [
"Post-war suburbs expanded in the 1950s.",
"Urban sprawl increased pollution and traffic.",
"Skyscrapers defined city skylines in the 2000s."
],
"present": [
"Smart lights, sensors, and AI traffic control exist.",
"Electric buses and green roofs are common.",
"Cities use digital twins for planning."
],
"future": [
"AI-managed smart cities will dominate by 2040.",
"Autonomous underground delivery tunnels will spread.",
"Climate-adaptive buildings will emerge post-2035."
]
},
"fashion": {
"past": [
"Fast fashion exploded in the 2000s.",
"Sewing machines revolutionized clothing in the 1800s.",
"Fashion trends were driven by magazines and TV."
BRIDING RISK AND INNOVATION IN GENERATIVE AI 34
 ],
"present": [
"AI personal stylists suggest outfits.",
"Digital fashion is used in the metaverse.",
"Wearables track health and style."
],
"future": [
"Smart fabrics will change color or temperature.",
"AI-generated designs will dominate by 2030.",
"Virtual fashion shows in VR will become standard."
]
},
"defense": {
"past": [
"World Wars advanced radar, aircraft, and missiles.",
"Cold War spurred nuclear and satellite tech.",
"Drones first saw combat use in early 2000s."
],
"present": [
"AI powers battlefield drones and surveillance.",
"Robotic dogs and exosuits are in trial phases.",
"Cyber defense is a military priority in 2025."
],
"future": [
"Autonomous combat units will deploy by 2035.",
"Laser weapons and EMPs will replace missiles.",
BRIDING RISK AND INNOVATION IN GENERATIVE AI 35
 "AI will command battlefield strategy by 2040."
]
}
}

def predict(topic, time):

topic = topic.lower()
time = time.lower()
if topic in event_timeline and time in event_timeline[topic]:
predictions = event_timeline[topic][time]
if predictions:
prediction = random.choice(predictions)
else:
prediction = f"No {time} predictions available for {topic}."
else:
prediction = f"❌ '{topic}' or '{time}' not found."
display(Markdown(f"### {time.capitalize()} Prediction for
{topic.capitalize()}:\n> {prediction}"))

def predict_all_for_topic(topic):
topic = topic.lower()
if topic in event_timeline:
md = f"## All Predictions for {topic.capitalize()}\n"
for t in ["past", "present", "future"]:
md += f"### {t.capitalize()}:\n"
for i, p in enumerate(event_timeline[topic].get(t, []), 1):

BRIDING RISK AND INNOVATION IN GENERATIVE AI 36

 md += f"{i}. {p}\n"
md += "\n"
else:
md = f"❌ Topic '{topic}' not found."
display(Markdown(md))

topic_dropdown = widgets.Dropdown(
options=list(event_timeline.keys()),
value='technology',
description='Select Topic:',
style={'description_width': 'initial'}
)

time_dropdown = widgets.Dropdown(
options=['past', 'present', 'future'],
value='future',
description='Select:',
style={'description_width': 'initial'}
)

show_button = widgets.Button(description="Show One ", button_style='success')

print_topic_button = widgets.Button(description="Show All The Topics ",
button_style='warning')

BRIDING RISK AND INNOVATION IN GENERATIVE AI 37

output = widgets.Output()

def on_show_clicked(b):
output.clear_output()
with output:
predict(topic_dropdown.value, time_dropdown.value)

def on_topic_clicked(b):
output.clear_output()
with output:
selected_topic = topic_dropdown.value
selected_time = time_dropdown.value

if selected_topic in event_timeline and selected_time in

event_timeline[selected_topic]:
predictions = event_timeline[selected_topic][selected_time]
if predictions:
md = f"## {selected_time.capitalize()} Predictions for
{selected_topic.capitalize()}\n"
for i, pred in enumerate(predictions, 1):
md += f"{i}. {pred}\n"
else:
md = f"No {selected_time} predictions available for {selected_topic}."
else:
md = f"❌ Topic '{selected_topic}' or time '{selected_time}' not found."

BRIDING RISK AND INNOVATION IN GENERATIVE AI 38

 display(Markdown(md))

show_button.on_click(on_show_clicked)
print_topic_button.on_click(on_topic_clicked)

display(Markdown("## Bridging Risk and Innovation: Generation AI"))

display(topic_dropdown, time_dropdown, show_button, print_topic_button,
output)

BRIDING RISK AND INNOVATION IN GENERATIVE AI 39

 CHAPTER-9

BRIDING RISK AND INNOVATION IN GENERATIVE AI 40

 SYSTEM STUDY
9.1 FEASIBILITY STUDY
A feasibility study is an essential part of system development that evaluates whether a project is
practical and achievable within the given constraints such as technology, cost, and usability.
Before implementing any solution, it is important to determine whether the proposed system will
be effective, efficient, and sustainable.
For the project "Bridging Risk and Innovation: Generative AI in Scenario Creation," this study
helps to understand the practicality of deploying an AI-based system that generates future
scenarios using influence events, clustering, and AI-driven projections. The system must be
technically sound, economically viable, and operationally convenient for users.
Three key considerations involved in the feasibility analysis are,
 TECHNICAL FEASIBILITY
 ECONOMICAL FEASIBILITY
 OPERATIONAL FEASIBILITY
9.1.1 TECHNICAL FEASIBILITY
The proposed system is technically feasible due to the availability of reliable and accessible
machine learning tools. It utilizes open-source frameworks such as Python, sentence-transformers,
scikit-learn, and optionally OpenAI’s GPT models. The core functionality like embedding,
clustering, and scenario generation can run on standard hardware (no GPU required). The modular
design allows easy maintenance, testing, and future enhancements. Thus, the project is well within
current technological capabilities.
9.1.2 ECONOMICAL FEASIBILITY
From a financial perspective, the system is cost-efficient and suitable for academic or prototype-
level deployment. All major components are built using open-source software, avoiding the need
for expensive licenses. Simulated outputs eliminate the need for premium APIs during
development. If cloud-based models (e.g., GPT-4) are integrated in the future, the system can still
be controlled within a limited budget. Overall, the low development and execution cost makes it
economically viable.
9.1.3 OPERATIONAL FEASIBILITY
Operationally, the system is user-friendly and requires minimal input. Users can enter natural
language prompts without needing technical knowledge. The back-end automates the entire
workflow — from event filtering and clustering to scenario output. The design supports various
application domains like climate, education, governance, and more. Its adaptability and ease of
use ensure that even non-technical users such as students, planners, or policymakers can operate it
effectively. Hence, the system is practical for real- world or institutional usage.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 41

 CHAPTER -10

BRIDING RISK AND INNOVATION IN GENERATIVE AI 42

 SYSTEM TESTING

10.1 SYSTEM TESTING

The purpose of testing is to discover errors and validate that the developed system meets the
intended requirements. Testing verifies that the system components work as expected both
individually and when integrated. Our testing focused on ensuring the functionality, consistency,
and reliability of the scenario generation and risk analysis system using generative AI models.
10.1.1 Unit Testing
Unit testing was performed for the core modules such as:
 Prompt Input Handler
 Preprocessing Unit
 Scenario Generator (GPT-based)
 Evaluation and Consistency Scorer
 Each function and logic unit was tested individually to verify the correctness of output for
a given input and check that the module performed expected tasks like embedding
generation, clustering, and scoring.
10.1.2 Integration Testing
All modules were tested together to ensure they interact correctly. Integration testing validated the
flow from:
Prompt input → Preprocessing → Scenario generation → Filtering → Evaluation → Output
Rendering
The integrated model was checked for input/output consistency and performance across various
test prompts.
10.1.3 Functional Testing
The system was tested for the following functional requirements:
 Accept valid prompts and generate plausible future scenarios
 Cluster influence events accurately
 Produce consistent projections from clustered data
 Score and rank generated scenarios based on relevance
Test items included:
 Valid Input: Accepts meaningful and syntactically correct prompts.
 Invalid Input: Detects and discards empty or unrelated prompts.
 Functions: Triggers all modules correctly.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 43

  Output: Generates formatted scenario text with scores.
 Procedure: Simulates end-to-end flow with scenario visualization.
10.2 SYSTEM TESTING
The entire system was validated to ensure that it meets performance and accuracy expectations.
A full run from real-time data simulation to scenario projection was tested.
10.2.1 White Box Testing
White Box Testing was conducted to verify the internal logic, control flow, and modular
structure of the code. Since the system includes core functions like semantic embedding,
clustering, and consistency scoring, white-box testing ensured each function behaved as
expected internally.
 Verified loop structures, conditional branches, and exception handling in Python
functions.
 Checked internal data transformation steps between model encoding, clustering, and
scoring.
 Ensured that the embeddings passed through the clustering module were not altered or
corrupted.
Purpose: To test functions from within the system and validate internal correctness at the code
level.
10.2.2 Black Box Testing
Black Box Testing was carried out to test the system without knowledge of the internal code
logic. Only input prompts and expected scenario outputs were considered.
 Prompts like “What happens if climate policies are delayed?” were given as input.
 The system was expected to generate 2–3 realistic scenario narratives with relevance
to the prompt.
Purpose: To validate the system from the user's point of view—ensuring it works correctly
even without understanding internal operations.
2. Unit Testing
 Unit testing was conducted on the following components:
 Input prompt handler
 Scenario generator
 Event clustering
 Scenario scorer
Test Strategy and Approach
 Manual testing was used to validate outputs.
 Scenarios and scores were printed and reviewed for contextual relevance.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 44

Test Objectives
 Validate meaningful outputs for different inputs
 Ensure top scenario is always selected from combinations.
 Confirm input/output integrity at each module.
Features to Be Tested
 Prompt acceptance and logging
 Scenario generation
 Projection scoring and ranking
 Output formatting and clarity
3. Integration Testing
This testing was focused on ensuring smooth flow between modules such as:
User prompt → Embedding → Clustering → Scenario Generation → Final Output
Test Result:
All modules integrated successfully. No interface failures detected between any two components.
4. Acceptance Testing
Acceptance Testing involved:
 Running end-to-end tests with real-time data (simulated news-like articles)
 Evaluating whether the generated scenarios were:
 Coherent
 Relevant
 Diverse
Test Result:
All generated scenarios were meaningful and interpretable by users. No major failures observed.
System passed acceptance criteria.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 45

BRIDING RISK AND INNOVATION IN GENERATIVE AI 46
 CHAPTER-11

BRIDING RISK AND INNOVATION IN GENERATIVE AI 47

 OUTPUT SCREENSHOT
11.1 OUTPUT

HOME PAGE

BRIDING RISK AND INNOVATION IN GENERATIVE AI 48

 11.2 OUTPUT

BRIDING RISK AND INNOVATION IN GENERATIVE AI 49

 11.3 OUTPUT

BRIDING RISK AND INNOVATION IN GENERATIVE AI 50

 CHAPTER-12

BRIDING RISK AND INNOVATION IN GENERATIVE AI 51

 CONCLUSION

The project titled “Bridging Risk and Innovation: Generative AI in Scenario Creation” presents a
novel and effective approach to modernizing the scenario planning process by utilizing the
capabilities of Generative Artificial Intelligence. Traditional methods of scenario generation are
often time-consuming, manual, and limited in creativity, relying heavily on expert knowledge and
historical data. In contrast, this system offers a more intelligent and scalable solution by combining
real-time context retrieval, semantic embedding, clustering of influence events, and AI-driven
narrative generation. The implementation successfully demonstrates how current models can
generate plausible future projections, evaluate them for logical consistency, and select the most
coherent scenarios, addressing the limitations of older GPT-based systems that lack updated
knowledge beyond 2020. By simulating real-world use cases across domains like policy,
environment, technology, and society, the system proves to be both adaptable and relevant for
today’s uncertain decision-making landscape. The project was tested thoroughly at each stage—
input handling, projection generation, and scenario selection—to ensure accuracy, clarity, and
functional integrity. Overall, this work not only bridges the gap between outdated AI models and
present-day planning needs but also provides a foundational system that can be extended for real-
time risk assessment, strategic foresight, and responsible innovation across industries

BRIDING RISK AND INNOVATION IN GENERATIVE AI 52

 CHAPTER-13

BRIDING RISK AND INNOVATION IN GENERATIVE AI 53

 FUTURE ENHANCEMENT
FUTURE SCOPE Integration with Real-Time Data Sources:
The system can be extended to fetch real-time data from news APIs, policy databases, and global
updates to dynamically update the influence event corpus.
Deployment with Advanced AI Models:
Future scope includes integrating cutting-edge LLMs like GPT-4 or domain-adapted transformers
for more refined, human-like scenario generation.
Development of a Web or Mobile Interface:
A visual interface can be built for easier input, viewing, and export of scenario results, making the
system accessible to non-technical users and decision-makers.
Cross-Domain Scenario Generation:
The framework can be applied to multiple domains such as healthcare, agriculture, education,
urban planning, and disaster response by customizing datasets and prompts.
Scenario Database and Storage System:
Enhancing the project to store past prompts, generated scenarios, and consistency scores in a
structured database would allow historical comparisons and tracking.
User Feedback Learning Loop:
Implementing a system for users to rate or comment on generated scenarios can help improve
future outputs using adaptive AI learning mechanisms.
Ethical and Bias Detection Modules:
Future updates can include ethical filters, content moderation systems, and fairness evaluation to
ensure responsible AI usage.
Support for Multiple Languages:
Enabling multi-language input and output will expand the reach of the system globally, supporting
regional planning and localization.
Interactive Visualization Tools:
Future enhancements may involve graph-based visualizations for scenario trees, projection
timelines, and consistency networks to better interpret results.
Integration with Decision Support Systems:
The system can be extended to support government or business decision-making platforms,
enhancing their risk forecasting and strategic planning capabilities

BRIDING RISK AND INNOVATION IN GENERATIVE AI 54

 CHAPTER-14

BRIDING RISK AND INNOVATION IN GENERATIVE AI 55

 REFERENCES
1. Inayatullah, S. (2008). The futures triangle and scenario building. Foresight, 10(1), 4–8.
 Introduced the Futures Triangle method to visualize past, present, and future drivers.
2. Amer, M., Daim, T. U., & Jetter, A. (2013). A review of scenario planning. Futures, 46, 23
40.
 Provided a classification and analysis of various scenario planning techniques.
3. Daheim, C., & Uerz, G. (2006). Scenario planning: A tool for navigating strategic risks.
The Foresight Report.
 Proposed structured frameworks for identifying and preparing for future risks.
4. Floridi, L., & Chiriatti, M. (2020). GPT-3: What’s it good for? Philosophy & Technology,
34, 731–734.
 Discussed the limitations and implications of large language models like GPT-3.
5. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines.
Nature Machine Intelligence, 1(9), 389–399.
 Summarized international efforts in creating responsible AI guidelines.
6. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of
algorithms: Mapping the debate. Big Data & Society, 3(2).
 Explored ethical challenges and societal risks of automated decision-making systems.
7. Holtz, P., Sobe, T., & Others. (2023). Bridging Risk and Innovation: Generative AI in
Scenario Creation. Research Paper.
 The base paper on which this mini project is built; discussed scenario generation using
GPT-like models and consistency scoring.
8. Bughin, J., Seong, J., Manyika, J., et al. (2019). Artificial Intelligence and the future of
growth. McKinsey Global Institute.
 Analyzed how AI can influence long-term economic growth and innovation.
9. Truby, J. (2020). Governing AI: A call for international regulation. Law, Innovation and
Technology, 12(2), 241–260.
 Highlighted the importance of legal frameworks to govern advanced AI systems.
10. OpenAI. (2023). ChatGPT Technical Report. https://openai.com/research
 Detailed capabilities and use-cases of GPT-3.5 and GPT-4 models.

BRIDING RISK AND INNOVATION IN GENERATIVE AI 56