Causal Impact of Algorithmic Bias on Societal Decision-Making

Instruction: Develop a comprehensive study design to evaluate the causal effects of algorithmic bias in AI systems on decision-making in societal contexts.

Context: This question asks you to create a study design that measures the causal impact of algorithmic biases (e.g., in machine learning models used for loan approval, job screening) on societal decisions. Discuss methodologies to identify and measure these biases, consider potential confounders, and suggest ways to mitigate the effect of these biases in the causal analysis.

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Make the intervention concrete. Compare a specified decision policy or model version with a defined alternative, rather than treating an undefined quantity called algorithmic bias as the treatment. For example, evaluate how a revised screening policy changes selection and subsequent outcomes among the same eligible applicant population.

First audit measured disparities, data quality, missing groups and label construction...

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