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To address such biases:
● Inclusive data collection: Ensure the training data includes diverse representations from various
demographics, including rural areas. This helps the AI system to learn patterns that are applicable to
a broader range of applicants.
● Bias detection and correction: Implement algorithms that detect and correct biases during the
evaluation process. This can involve techniques such as fairness constraints and bias mitigation
strategies.
● Human oversight: Complement AI assessments with human judgement to catch and mitigate unfair
decisions. Loan officers can review flagged applications to ensure qualified candidates are not unjustly
denied.
● Transparency: Make the AI decision-making process transparent so applicants understand why their
applications are accepted or denied. Providing clear reasons for decisions can help build trust and
allow applicants to address any specific issues.
Assertion and Reasoning Questions:
Direction: Questions 2-4, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions
by selecting the appropriate option given below:
a. Both A and R are true and R is the correct explanation of A.
b. Both A and R are true but R is not the correct explanation of A.
c. A is true but R is false.
d. A is false but R is true.
2. Assertion (A): Bias can arise at various stages of AI development, including data collection, model training, and
system deployment.
Reason (R): Tackling bias at these stages is crucial for the ethical development and use of AI systems.
3. Assertion (A): Privacy and security risks are significant when designing AI to replicate human life.
Reason (R): Human-like AI systems often need access to personal data, which, if misused, can lead to privacy
breaches or security threats.
4. Assertion (A): Transparency and explainability are crucial in AI systems to promote trust and accountability.
Reason (R): Transparency ensures that users understand how AI systems make decisions, while explainability
provides insight into the reasoning behind those decisions. This promotes trust by allowing users to verify the
fairness and reliability of AI systems. Additionally, accountability is enhanced when the decision-making process
is transparent, as it enables stakeholders to identify and address any biases or errors in the AI system.
Ans. 2. a 3. a 4. d

Unsolved Questions
SECTION A (Objective Type Questions)
AI QUIZ
A. Tick ( ) the correct option.
1. Choose the reason why bias in AI is considered a major challenge.
a. AI systems can only be used for specific tasks

b. Bias can lead to unethical and unfair consequences
c. AI development is too expensive
d. AI systems are difficult to interpret

2. Which of the following of the following can be a result of algorithmic bias in AI systems?
a. Fair and equal treatment of all users b. Increased efficiency and accuracy

c. Unfair or discriminatory outcomes d. Enhanced user satisfaction

284 Touchpad Artificial Intelligence - XI