Unit–1: Revisiting AI Project Cycle & Ethical Frameworks for AI
1. Outline the main steps in the AI Project Cycle briefly.
The AI project cycle is the cyclical process followed to complete an AI project. The
AI Project Cycle mainly has 6 stages:
Problem Scoping- you set the goal for your AI project by stating the problem which
you wish to solve with it. Under problem scoping, we look at various parameters
which affect the problem we wish to solve so that the picture becomes clearer.
Data Acquisition-You need to acquire data which will become the base of your
project as it will help you understand what the parameters that are related to problem
scoping are. You go for data acquisition by collecting data from various reliable and
authentic sources.
Data Exploration-Since the data you collect would be in large quantities, you can try
to give it a visual image of different types of representations like graphs, databases,
flow charts, maps, etc. This makes it easier for you to interpret the patterns which
your acquired data.
Modelling-After exploring the patterns, you can decide upon the type of model you
would build to achieve the goal. For this, you can research online and select various
models which give a suitable output.
Evaluation -you can test your model on some newly fetched data. The results will
help you in evaluating your model and improving it.
Deployment-The deployment stage is crucial for ensuring the successful integration
and operation of AI solutions in real-world environments, enabling them to deliver
value and impact to users and stakeholders.
2.What roles does computer vision play in agricultural monitoring systems?
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. Mention the factors which knowingly or unknowingly influence our decision-
making.
1. Value of humans Value of non-humans
2. Is the decision I am taking aligned with my religious views?
3. Intuition & Values-Does what I am thinking sound correct?
4. What is the necessity for Ethical Frameworks in AI development?
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.
5. Mention the key characteristics of 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. Sector-based ethical frameworks may also
apply to domains such as finance, education, transportation, agriculture, governance, and law
enforcement.
6. What do you mean by 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.
7.What is Natural Language Processing? Explain any two real-life applications of NLP.
Natural Language Processing, abbreviated as NLP, is a branch of artificial intelligence that
deals with the interaction between computers and humans using the natural language. Natural
language refers to language that is spoken and written by people, and natural language
processing (NLP) attempts to extract information from the spoken and written word using
algorithms.
The ultimate objective of NLP is to read, decipher, understand, and make sense of
humanlanguages in a valuable manner.
Examples of Natural Language Processing
Email filters
Email filters are one of the most basic and initial applications of NLP online. It started with
spam filters, uncovering certain words or phrases that signal a spam message.
Machine Translation
NLP is used in machine translation systems like Google Translate and Microsoft Translator to
automatically translate text from one language to another.
8. How do value-based frameworks contribute to ethical decision-making by
emphasizing fundamental principles and values?
Value-based frameworks focus on fundamental ethical principles and values guiding
decision-making. It reflects the different moral philosophies that inform ethical reasoning.
Value-based frameworks are concerned with assessing the moral worth of actions and guiding
ethicalbehaviour. They can be further classified into three categories:
i. 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.
ii. 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.
iii. 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.
