FBISE, NCP & NBF-aligned resources for Classes 9–12
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Computer Chapter 5 : Topic 2.1
AI Bias
1 / 21
A company uses an AI system to screen job applications. Although it successfully selects many qualified candidates, it repeatedly rejects applicants from a particular group despite having similar qualifications. What social issue does this situation best illustrate?
Reasoning: Algorithmic bias occurs when AI unfairly favors or disadvantages certain groups.
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An AI-based loan approval system helps banks process applications quickly, but some deserving applicants are denied loans because the system relies heavily on past data. What is the main concern?
Reasoning: AI decisions can disadvantage individuals if the data or rules used are unfair.
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A school uses AI to predict which students may need extra support. Most predictions are accurate, but some hardworking students are incorrectly identified as low performers. Which limitation of AI is demonstrated?
Reasoning: AI predictions are often based on patterns and may not correctly represent every individual.
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Why might an AI system that works well for the majority of people still create social concerns?
Reasoning: AI systems optimized for the majority can overlook the needs of smaller groups.
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A hospital uses AI to prioritize patients for treatment. Most patients benefit, but some rare medical conditions are not recognized properly. What does this example suggest?
Reasoning: AI may struggle with rare or underrepresented cases.
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An AI-powered recruitment system is trained using data from previous successful employees. As a result, it favors candidates with similar backgrounds. What social implication could arise?
Reasoning: AI can unintentionally repeat past biases found in training data.
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Which statement best explains why AI decisions should sometimes be reviewed by humans?
Reasoning: Human oversight can help identify unfair or harmful outcomes.
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A facial recognition system accurately identifies most people but frequently makes mistakes for certain ethnic groups. What issue does this demonstrate?
Reasoning: Bias occurs when AI performs differently across groups.
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Which factor is most likely to cause unfair AI decisions?
Reasoning: AI learns from data, so biased data can produce biased outcomes.
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An AI system determines insurance premiums based on historical records. Some customers are charged more due to patterns in old data that no longer reflect reality. What concern does this raise?
Reasoning: Historical data may contain unfair patterns that AI continues to use.
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Why is diversity in training data important when developing AI systems?
Reasoning: Diverse data helps AI represent a wider range of people and situations.
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An AI system recommends prison sentences based on previous court records. Critics argue that some groups receive harsher recommendations. What ethical concern is being highlighted?
Reasoning: Biased AI decisions can have serious consequences in legal systems.
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Which scenario best demonstrates AI harming a small group while benefiting most users?
Reasoning: The system benefits many applicants but unfairly disadvantages a specific group.
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What is the primary reason that AI systems may not always make perfectly fair decisions?
Reasoning: AI often reflects the strengths and weaknesses of its training data.
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A company relies entirely on AI to evaluate employee performance. Some workers feel their unique contributions are ignored because the AI only measures specific metrics. What limitation of AI is shown?
Reasoning: AI may fail to consider qualities that are difficult to measure numerically.
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Which action would best reduce the risk of unfair AI decision-making?
Reasoning: Monitoring AI outcomes helps identify and correct unfair behavior.
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An AI admission system selects students based mainly on examination scores. Creative and talented students with average scores are often rejected. What does this indicate?
Reasoning: AI can only evaluate the information it has been programmed or trained to use.
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Why should society be concerned if AI systems make important decisions without transparency?
Reasoning: Lack of transparency makes it difficult to identify errors or unfair treatment.
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A predictive policing AI sends more police patrols to certain neighborhoods because historical crime data is higher there. What potential problem could result?
Reasoning: AI may continue patterns from historical data even if they are unfair.
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Which statement best summarizes the social implication of AI decision-making?
Reasoning: AI offers many benefits, but unfair outcomes can occur without careful design and oversight.
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What is the most responsible approach when using AI to make decisions that significantly affect people's lives?
Reasoning: Fairness, transparency, and human review help reduce harmful consequences of AI decisions.
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