Part B
Unit 1- Revisiting AI Project Cycle & Ethical Frameworks for AI
1. Define AI Project Cycle.
The AI Project Cycle provides us with an appropriate framework which can lead us towards
the goal. The AI project cycle is the cyclical process followed to complete an AI project.
The AI Project Cycle mainly has 6 stages:
Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
Deployment
2. Explain Domains of AI.
With respect to the type of data fed in the AI model, AI models can be broadly categorized
into three domains:
o Statistical Data
o Computer Vision
o Natural Language Processing
Statistical Data:
Statistical Data is a domain of AI related to data systems and processes, in which the
system collects numerous data, maintains data sets and derives meaning out of them.
The information extracted through statistical data can be used to make a decision about it.
Example of Statistical Data
o Price Comparison Websites
These websites are being driven by lots and lots of data and comparing the price of a
product from multiple vendors in one place. PriceGrabber, PriceRunner, Junglee,
Shopzilla, DealTime are some examples of price comparison websites. Price comparison
websites can be found in almost every domain such as technology, hospitality,
automobiles, durables, apparel, etc.
Computer Vision
Computer Vision (CV), is a domain of AI that depicts the capability of a machine to get and
analyse visual information and predict some decisions about it.
The entire process involves image acquiring, screening, analysing, identifying and
extracting information.
In computer vision, Input to machines can be photographs, videos and pictures from
thermal or infrared sensors, indicators and different sources.
Computer vision related projects translate digital visual data into descriptions. This data is
then turned into computer readable language to aid the decision making process. The main
objective of this domain of AI is to teach machines to collect information from pixels.
Examples of Computer Vision
o Agricultural Monitoring
Computer vision is employed in agriculture for crop monitoring, pest detection, and
yield estimation. Drones with cameras capture aerial images of farmland, which are
then analysed to assess crop health and optimize farming practices.
3. What is the necessity for Ethical Frameworks in AI development?
As we have seen how bias could result in unwanted outcomes in AI solutions. Think of
the hiring algorithm which was biased against women applicants. Ethical frameworks ensure
that AI makes morally acceptable choices. If we use ethical frameworks while building our AI
solutions, we can avoid unintended outcomes, even before they take place.
4. Explain the types of Ethical Freameworks.
Ethical frameworks for AI can be categorized into two main types: sector-based and value- based
frameworks.
1. Sector-based Frameworks:
These are frameworks tailored to specific sectors or industries. In the context of AI,
one common sector-based framework is Bioethics, which focuses on ethical considerations in
healthcare. It addresses issues such as patient privacy, data security, and the ethical use of
AI in medical decision making. Ex. Sector-based ethical frameworks are finance, education,
transportation, agriculture, governance, and law enforcement.
2. Value-based Frameworks:
Rights-based: Prioritizes the protection of human rights and dignity, valuing human life
over other considerations. It emphasizes the importance of respecting individual
autonomy, dignity, and freedoms. In the context of AI, this could involve ensuring that
AI systems do not violate human rights or discriminate against certain groups.
Utility-based: Evaluates actions based on the principle of maximizing utility or overall
good, aiming to achieve outcomes that offer the greatest benefit and minimize harm. It
seeks to maximize overall utility or benefit for the greatest number of people. In AI,
this might involve weighing the potential benefits of AI applications against the risks
they pose to society, such as job displacement or privacy concerns.
Virtue-based: This framework focuses on the character and intentions of the
individuals involved in decision-making. It asks whether the actions of individuals or
organizations align with virtuous principles such as honesty, compassion, and integrity.
In the context of AI, virtue ethics could involve considering whether developers, users,
and regulators uphold ethical values throughout the AI lifecycle.
5. Explain Bioethics.
Bioethics is an ethical framework used in healthcare and life sciences. It deals with
ethical issues related to health, medicine, and biological sciences, ensuring that AI
applications in healthcare adhere to ethical standards and considerations.
6. Mention the principles of Bioethics.
Principles of bioethics:
• Respect for Autonomy.
• Do not harm.
• Ensure maximum benefit for all.
• Give justice.
