AI Bias and the Future of Work and Meaning of Life

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What is the primary cause of AI bias?

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Bias is introduced through data selection, design, and human influence.

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As HR Manager, what step would you take to mitigate bias in AI screening that favors candidates with a similar background to previous hires, resulting in fewer women and a minority of applicants being selected?

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Retrain the AI using a broader dataset while ensuring fairness checks.

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A government introduces AI-powered facial recognition as a safety precaution, increasing security, but it disproportionately misidentifies racial groups. What policy should be put in place? Options: Ban facial recognition to promote equality, Require AI decisions to be reviewed by human authorities, Improve AI Training Data, Allow unrestricted use of AI surveillance for security purposes

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Improve AI Training Data

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A company starts replacing their employees with AI to reduce costs, as a result many employees lose their jobs. As a business leader, how would you address this issue? Options: Proceed with the replacement of employees, as it also increases the company’s efficiency. Offer retraining programs for displaced workers to transition to new roles. Ban AI automation to protect human jobs. Use AI only for assistance rather than full job replacement.

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Offer retraining programs for displaced workers to transition to new roles.

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A bank uses AI to approve loans. The system denies loans to applicants from a specific zip code, indirectly discriminating against minority communities. What should the bank do?

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Adjust the AI model to remove zip code as a factor.

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Which of the following is the best way to reduce AI bias? Actively auditing, testing, and retraining AI with diverse datasets., Ensuring AI is exposed to a large amount of random data., Removing all human influence from AI training., Allowing AI to self-correct over time through machine learning.

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Actively auditing, testing, and retraining AI with diverse datasets.

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Which of the following does NOT contribute to AI bias? Lack of diversity in training data., Human cognitive biases influencing AI design., Algorithmic adjustments that increase fairness., Historical inequalities embedded in data.

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Algorithmic adjustments that increase fairness.

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Self-driving cars must make split-second ethical decisions in accidents. What should be the guiding principle?

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Implement ethical decision-making frameworks, considering all possible factors.

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Why is human oversight still important in AI decision-making?

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Humans provide emotional reasoning that AI lacks.

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Why is AI bias difficult to eliminate completely?

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Bias often reflects societal inequalities that are difficult to remove from data.