# The Authority of "Fair" in Machine Learning

arXiv: https://arxiv.org/abs/1706.09976 · HTML: https://mlsanity.com/paper/1706.09976/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Fair Decision-making Under Uncertainty](https://mlsanity.com/paper/2301.12364/) (2023)
- [Fairness Certification for Natural Language Processing and Large Language Models](https://mlsanity.com/paper/2401.01262/) (2024)

## Abstract (arXiv)

In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.