# kaal:claim:5541658-013

**Claim.** 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.

**Type.** failure  **Support.** argued

**Holds when.**

- machine learning models trained on historical judicial data

**Source quote.**

> 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.

**From.** Wulf A. Kaal, Morgan A. Gray, *The Evolving Role of Artificial Intelligence in Law* (2025), Bias in AI Judicial Systems, page 21

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

**Verify.** sha256 of source PDF `e543a2d698fcd522d4d02e034cc9ee1344d0015d2c824b40b9e05ab7c0728c60` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20and%20Gray%20-%202025%20-%20The%20Evolving%20Role%20of%20Artificial%20Intelligence%20in%20Law.pdf

**Failure mode.** fairness through unawareness failure  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, education-and-practice

**Keywords.** algorithmic-bias, fairness-through-unawareness, proxy-variables, training-data

**Related claims.**

- specializes: https://wulfkaal.github.io/claims/4855607-003

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