# Renyi Fair Information Bottleneck for Image Classification

Source: [Renyi Fair Information Bottleneck for Image Classification](<https://www.sota2.com/research/paper/68fa593f1e209131ce9b552e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:00.930Z

Authors: Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina

Published: 2022-03-09T18:52:04.000Z

## Abstract

We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB). We consider two different fairness constraints - demographic parity and equalized odds - for learning fair representations and derive a loss function via a variational approach that uses Renyi's divergence with its tunable parameter $\\alpha$ and that takes into account the triple constraints of utility, fairness, and compactness of representation. We then evaluate the performance of our method for image classification using the EyePACS medical imaging dataset, showing it outperforms competing state of the art techniques with performance measured using a variety of compound utility/fairness metrics, including accuracy gap and Rawls' minimal accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.04950>)

[PDF](<https://arxiv.org/pdf/2203.04950.pdf>)