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Can Unbiased Predictive AI Amplify Bias?

Author

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  • Tanvir Ahmed Khan

Abstract

I analyze a model of unbiased predictions mediated by biased humans, showing that a bias-neutral precision gain (e.g. from AI adoption) is not generally bias-neutral in its effects. Expected victims of bias are discriminated-group applicants with qualifications warranting approval under a counterfactual unbiased standard but not the stricter biased standard they face. For them, precision raises discrimination by reducing noise-driven chance approvals. Overall discrimination can also increase when precision shifts more realized predictions of discriminated-group applicants into than out of the between-standards interval. This isolates a new channel through which AI can exacerbate discrimination, distinct from biased design or data.

Suggested Citation

  • Tanvir Ahmed Khan, 2026. "Can Unbiased Predictive AI Amplify Bias?," Working Paper 1510, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1510
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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