kaal:claim:5541658-013

Bias in judicial AI arises because models are trained on historical data that reflect past inequities, and the standard remedy of fairness through unawareness, meaning the omission of protected characteristics such as race, fails because proxy variables continue to correlate with the omitted attribute.

This bias arises because AI models rely on historical data that reflect past inequities, and even attempts at "fairness through unawareness" (omitting protected characteristics like race) fail due to proxy variables that correlate with bias.

Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025), Bias in AI Judicial Systems, p. 21
https://ssrn.com/abstract=5541658 · source PDF

Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

• machine learning models trained on historical judicial data

failuresupport: arguedfailure: fairness through unawareness failurefamily: ai-model-and-training-failureai-and-agentseducation-and-practice

• specializes kaal:claim:4855607-003 Deep learning models inadvertently learn and amplify whatever biases exist in their training data, so the comp...

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