# AI Ethics and Algorithmic Bias
Status: public
Confidence: medium (0.82) (verified)
Last verified: 2026-05-28
Generation: ai_structured


## TL;DR

AI ethics and bias work asks whether AI systems distribute errors, benefits, and harms fairly across people and groups.

## Core Explanation

Bias can enter through data collection, labels, model objectives, evaluation choices, and deployment context. Fairness is not a single universal metric; different fairness definitions can emphasize different goals and may conflict.

## Evidence Notes

The previous version mixed official, advocacy, and future-looking sources. This repair keeps the article public but lowers confidence and aligns claims to PMLR, ACM, and European Commission sources.

## Further Reading

- [Gender Shades - PMLR](https://proceedings.mlr.press/v81/buolamwini18a.html)
- [A Survey on Bias and Fairness in Machine Learning](https://dl.acm.org/doi/10.1145/3457607)
- [AI Act - European Commission](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)

## Related Articles

- [AI Governance and Policy](ai-governance-and-policy.md)
- [AI Red Teaming and Safety](ai-red-teaming-and-safety.md)
- [AI Regulation Landscape](ai-regulation-landscape.md)