Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition

TL;DR

This paper proposes a rethinking of Automatic Gender Recognition systems by incorporating feedback mechanisms that allow correction, aiming to improve fairness and respect for individual gender identity beyond binary classifications.

Contribution

It introduces a feedback-based approach to AGR, enabling correction and re-evaluation, which enhances fairness and aligns with respecting gender diversity.

Findings

Feedback mechanisms can improve AGR fairness

Re-evaluation of system outputs supports gender diversity

Theoretical framework distinguishes sex, gender, and expression

Abstract

Automatic Gender Recognition (AGR) systems are an increasingly widespread application in the Machine Learning (ML) landscape. While these systems are typically understood as detecting gender, they often classify datapoints based on observable features correlated at best with either male or female sex. In addition to questionable binary assumptions, from an epistemological point of view, this is problematic for two reasons. First, there exists a gap between the categories the system is meant to predict (woman versus man) and those onto which their output reasonably maps (female versus male). What is more, gender cannot be inferred on the basis of such observable features. This makes AGR tools often unreliable, especially in the case of non-binary and gender non-conforming people. We suggest a theoretical and practical rethinking of AGR systems. To begin, distinctions are made between sex, gender, and gender expression. Then, we build upon the observation that, unlike algorithmic misgendering, human-human misgendering is open to the possibility of re-evaluation and correction. We suggest that analogous dynamics should be recreated in AGR, giving users the possibility to correct the system's output. While implementing such a feedback mechanism could be regarded as diminishing the system's autonomy, it represents a way to significantly increase fairness levels in AGR. This is consistent with the conceptual change of paradigm that we advocate for AGR systems, which should be understood as tools respecting individuals' rights and capabilities of self-expression and determination.