# Algorithmic bias

`kaal:entity:algorithmic-bias`

**Status.** derived

This node is assembled mechanically from the 4 claims that carry the concept tag `algorithmic-bias`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

4 claims across 2 works, 2024 to 2025.

**2024**

- [4941807-001](https://wulfkaal.github.io/claims/4941807-001) [failure/argued] *(failure mode)* -- Ex-post AI governance, in which regulation is applied only after AI systems have been developed and deployed or after large language models have already been pretrained on existing proprietary datasets, falls short of preemptively addressing the risks and biases those systems carry.
  > The traditional ex-post governance methods, where regulations are applied after AI systems are developed and deployed, or were pretrained LLMs models were trained on existing proprietary datasets, often fall short in preemptively addressing risks and biases.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807
- [4941807-010](https://wulfkaal.github.io/claims/4941807-010) [failure/argued] *(failure mode)* -- Strict data privacy regulation such as the GDPR imposes stringent conditions on data sharing that limit the amount and variety of data available to AI systems, which can reduce model performance and exacerbate bias because the training dataset is restricted.
  > GDPR imposes stringent conditions on data sharing, which can limit the amount and variety of data AI systems use, potentially reducing their performance and exacerbating biases due to the restricted dataset.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807
- [4941807-015](https://wulfkaal.github.io/claims/4941807-015) [failure/evidenced] *(failure mode)* -- A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory practices carried in the data used for training.
  > AI governance does encounter a critical challenge in mitigating biases within AI systems, where biases can inadvertently arise through algorithms incorporating discriminatory practices due to data used in training.
  Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

**2025**

- [5541658-013](https://wulfkaal.github.io/claims/5541658-013) [failure/argued] *(failure mode)* -- 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). SSRN: https://ssrn.com/abstract=5541658

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/algorithmic-bias.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.