# Active Fairness Instead of Unawareness

arXiv: https://arxiv.org/abs/2009.06251 · HTML: https://mlsanity.com/paper/2009.06251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting](https://mlsanity.com/paper/2302.08204/) (2023)
- [Fair Algorithm Design: Fair and Efficacious Machine Scheduling](https://mlsanity.com/paper/2204.06438/) (2022)

## Abstract (arXiv)

The possible risk that AI systems could promote discrimination by reproducing and enforcing unwanted bias in data has been broadly discussed in research and society. Many current legal standards demand to remove sensitive attributes from data in order to achieve "fairness through unawareness". We argue that this approach is obsolete in the era of big data where large datasets with highly correlated attributes are common. In the contrary, we propose the active use of sensitive attributes with the purpose of observing and controlling any kind of discrimination, and thus leading to fair results.