# Principal Fairness: Removing Bias via Projections

arXiv: https://arxiv.org/abs/1905.13651 · HTML: https://mlsanity.com/paper/1905.13651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 1 of 1)

- [Individual Preference Stability for Clustering](https://mlsanity.com/paper/2207.03600/) (2022)

## Abstract (arXiv)

Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention. We complement several recent papers in this line of research by introducing a general method to reduce bias in the data through random projections in a "fair" subspace. We apply this method to densest subgraph problem. For densest subgraph, our approach based on fair projections allows to recover both theoretically and empirically an almost optimal, fair, dense subgraph hidden in the input data. We also show that, under the small set expansion hypothesis, approximating this problem beyond a factor of 2 is NP-hard and we show a polynomial time algorithm with a matching approximation bound.