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● Cognitive bias: This refers to systematic patterns of deviation from rationality or objectivity in
judgement, influenced by factors like emotions and personal experiences. For example, a person
who strongly believes that climate change is not real might dismiss scientific evidence supporting it,
thus reinforcing their existing beliefs. Cognitive biases can lead to irrational or partial judgements,
impacting AI development and application.
Each of these biases can result in AI systems that unfairly discriminate against certain groups, leading to
unethical and unfair outcomes in various sectors such as healthcare, finance, and criminal justice.
4. List down the different key strategies to avoid Bias in AI.

Ans. Following are key strategies to avoid bias:
● Use diverse and representative data: The data used for training AI should properly represent
different groups of people who may be affected by AI decisions.
● Apply pre-processing and post-processing techniques: Before training the AI model, data should
be cleaned, balanced, and prepared carefully (pre-processing). After training, results should also
be checked and adjusted (post-processing).

● Develop fairness-aware algorithms: The algorithms or the rules that tell AI how to make decisions
should be designed with fairness in mind.

● Conduct regular audits and rigorous testing: AI systems should not be left unchecked after they
are created. They must go through frequent audits, monitoring, and testing to see how they perform
with real-world data.

● Involve diverse teams and domain experts: Creating fair AI requires a team of people from different
fields — including computer scientists, social scientists, ethicists, and people from underrepresented
communities.
● Promote transparency and explainability: AI systems should be designed so that their decisions
can be easily explained and understood by humans.
● Keep improving AI: AI is not a one-time project; it requires constant care and improvement.
Developers should regularly review, update, and monitor AI systems to make sure they stay in line
with current social values and adapt to changes in society.
● Collaborate across departments: To make AI fair and accountable, organisations should encourage
teamwork among different departments, such as data, legal, compliance, and policy teams.
● Follow ethical standards and regulation: Developers and organisations should follow clear ethical
codes and respect laws related to privacy, data use, and algorithmic fairness.

21 st
C. Competency-based questions: HOTS Century #Interdisciplinary
Skills
1. Aditi, a young entrepreneur, applies for a business loan to expand her small enterprise. She has an excellent
credit score, a solid business plan, and a history of successful repayments. However, the bank she approaches
uses an AI system to evaluate loan applications. This AI system was trained primarily on data from affluent urban
areas and does not consider factors relevant to Aditi’s rural context.
Despite her strong qualifications, the AI system flags her application as high risk because the training data does
not adequately represent rural entrepreneurs. As a result, Aditi’s loan application is denied, and she is unable to
expand her business. What are the potential consequences of using biased AI systems in financial services, and
how can such biases be addressed to ensure fair treatment of all applicants?
Ans. Using biased AI systems in financial services can lead to unfair treatment of certain groups, such as rural
entrepreneurs like: Aditi. This can result in qualified applicants being denied opportunities, perpetuating
economic disparities.

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