# Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It

arXiv: https://arxiv.org/abs/2407.10329 · HTML: https://mlsanity.com/paper/2407.10329/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2024.

## Built on by (most cited first, 7 of 7)

- [Variance reduction in output from generative AI](https://mlsanity.com/paper/2503.01033/) (2025)
- [Towards Effective Discrimination Testing for Generative AI](https://mlsanity.com/paper/2412.21052/) (2024)
- [Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies](https://mlsanity.com/paper/2408.06872/) (2024)
- [Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis](https://mlsanity.com/paper/2411.00631/) (2024)
- ["Draw me a curator" Examining the visual stereotyping of a cultural services profession by generative AI](https://mlsanity.com/paper/2508.07132/) (2025)
- [Generative AI in Heritage Practice: Improving the Accessibility of Heritage Guidance](https://mlsanity.com/paper/2510.13811/) (2025)
- [Prompt fidelity of ChatGPT4o / Dall-E3 text-to-image visualisations](https://mlsanity.com/paper/2510.21821/) (2025)

## Abstract (arXiv)

As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explores how genAI intersects with non-discrimination laws, identifying shortcomings and suggesting improvements. It highlights two main types of discriminatory outputs: (i) demeaning and abusive content and (ii) subtler biases due to inadequate representation of protected groups, which may not be overtly discriminatory in individual cases but have cumulative discriminatory effects. For example, genAI systems may predominantly depict white men when asked for images of people in important jobs. This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like unbalanced content, harmful stereotypes or misclassification. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws, including the AI Act, to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances.