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

arXiv: https://arxiv.org/abs/2407.17474 · HTML: https://mlsanity.com/paper/2407.17474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 71 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://mlsanity.com/paper/2211.05105/) (2022)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications](https://mlsanity.com/paper/2108.02818/) (2021)
- [Social Biases through the Text-to-Image Generation Lens](https://mlsanity.com/paper/2304.06034/) (2023)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology](https://mlsanity.com/paper/2112.14656/) (2021)
- [Understanding Unequal Gender Classification Accuracy from Face Images](https://mlsanity.com/paper/1812.00099/) (2018)
- [Stable Diffusion Exposed: Gender Bias from Prompt to Image](https://mlsanity.com/paper/2312.03027/) (2023)
- [The Stable Artist: Steering Semantics in Diffusion Latent Space](https://mlsanity.com/paper/2212.06013/) (2022)
- [Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence](https://mlsanity.com/paper/1907.07892/) (2019)
- [SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception](https://mlsanity.com/paper/1511.02459/) (2015)
- [Female Librarians and Male Computer Programmers? Gender Bias in Occupational Images on Digital Media Platforms](https://mlsanity.com/paper/1912.05474/) (2019)
- [Diversity in Sociotechnical Machine Learning Systems](https://mlsanity.com/paper/2107.09163/) (2021)
- [Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition](https://mlsanity.com/paper/2205.10049/) (2022)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)

## Built on by (most cited first, 1 of 1)

- [Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classification](https://mlsanity.com/paper/2601.11651/) (2026)

## Abstract (arXiv)

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.