  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.
