Anatomizing Bias in Facial Analysis

TL;DR

This paper reviews and categorizes existing algorithms for detecting, understanding, and reducing bias in facial analysis systems, highlighting current challenges and future directions.

Contribution

It provides a comprehensive taxonomy and overview of bias mitigation techniques in facial analysis, along with identifying open challenges in the field.

Findings

Systematic review of bias detection algorithms

Taxonomy of bias mitigation methods

Discussion of open challenges in biased facial analysis

Abstract

Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems. In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis. Our main contributions include a systematic review of algorithms proposed for understanding bias, along with a taxonomy and extensive overview of existing bias mitigation algorithms. We also discuss open challenges in the field of biased facial analysis.