Fair Representations by Compression

No. 13: AAAI-21 Technical Tracks 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Philosophy and Ethics of AI

Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data. We propose a novel unsupervised approach to map data into a compressed binary representation independent of sensitive attributes. We show that in an information bottleneck framework, a parsimonious representation should filter out information related to sensitive attributes if they are provided directly to the decoder. Empirical results show that the method achieves state-of-the-art accuracy-fairness trade-off and that explicit control of the entropy of the representation bit stream allows the user to move smoothly and simultaneously along both rate-distortion and rate-fairness curves.

10.1609/aaai.v35i13.17370