"My Kind of Woman": Analysing Gender Stereotypes in AI through The Averageness Theory and EU Law

TL;DR

This paper investigates how social stereotypes, particularly related to attractiveness, influence AI gender classification, revealing biases similar to human cognition and emphasizing the importance of multidisciplinary approaches for fair AI development.

Contribution

It introduces a novel analysis of gender bias in AI using the averageness theory and legal frameworks, highlighting the impact of human perceptions on AI fairness.

Findings

AI exhibits bias in gender classification based on attractiveness.

Human-like stereotypes are reflected in AI decision-making.

Legal and psychological insights can guide fair AI training practices.

Abstract

This study delves into gender classification systems, shedding light on the interaction between social stereotypes and algorithmic determinations. Drawing on the "averageness theory," which suggests a relationship between a face's attractiveness and the human ability to ascertain its gender, we explore the potential propagation of human bias into artificial intelligence (AI) systems. Utilising the AI model Stable Diffusion 2.1, we have created a dataset containing various connotations of attractiveness to test whether the correlation between attractiveness and accuracy in gender classification observed in human cognition persists within AI. Our findings indicate that akin to human dynamics, AI systems exhibit variations in gender classification accuracy based on attractiveness, mirroring social prejudices and stereotypes in their algorithmic decisions. This discovery underscores the critical need to consider the impacts of human perceptions on data collection and highlights the necessity for a multidisciplinary and intersectional approach to AI development and AI data training. By incorporating cognitive psychology and feminist legal theory, we examine how data used for AI training can foster gender diversity and fairness under the scope of the AI Act and GDPR, reaffirming how psychological and feminist legal theories can offer valuable insights for ensuring the protection of gender equality and non-discrimination in AI systems.