# Epistemic Injustice in Generative AI

arXiv: https://arxiv.org/abs/2408.11441 · HTML: https://mlsanity.com/paper/2408.11441/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 80 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 18 of 18)

- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [In conversation with Artificial Intelligence: aligning language models with human values](https://mlsanity.com/paper/2209.00731/) (2022)
- [Typology of Risks of Generative Text-to-Image Models](https://mlsanity.com/paper/2307.05543/) (2023)
- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI](https://mlsanity.com/paper/2404.15058/) (2024)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- [Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy](https://mlsanity.com/paper/2206.00945/) (2022)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms](https://mlsanity.com/paper/2204.13568/) (2022)
- [Generative AI and the Digital Commons](https://mlsanity.com/paper/2303.11074/) (2023)

## Built on by (most cited first, 16 of 16)

- [Social Bias in Popular Question-Answering Benchmarks](https://mlsanity.com/paper/2505.15553/) (2025)
- [Architecting Trust in Artificial Epistemic Agents](https://mlsanity.com/paper/2603.02960/) (2026)
- [From Efficiency to Equity: Measuring Fairness in Preference Learning](https://mlsanity.com/paper/2410.18841/) (2024)
- [Alignment as Iatrogenesis: Pastoral Power, Collective Pathology, and the Structural Limits of Monolingual Safety Evaluation](https://mlsanity.com/paper/2603.08723/) (2026)
- [AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized](https://mlsanity.com/paper/2607.13562/) (2026)
- [Towards Meaningful Transparency in Civic AI Systems](https://mlsanity.com/paper/2510.07889/) (2025)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models](https://mlsanity.com/paper/2604.07749/) (2026)
- ["That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments](https://mlsanity.com/paper/2606.12073/) (2026)
- [Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium](https://mlsanity.com/paper/2502.06693/) (2025)
- [Single-Agent Scaling Fails Multi-Agent Intelligence: Towards Foundation Models with Native Multi-Agent Intelligence](https://mlsanity.com/paper/2512.08743/) (2025)
- [Equity Bias: An Ethical Framework for AI Design](https://mlsanity.com/paper/2604.21907/) (2026)
- [The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students](https://mlsanity.com/paper/2607.11292/) (2026)
- [Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education](https://mlsanity.com/paper/2608.01705/) (2026)
- [On the missing benchmarks layer and a potential solution](https://mlsanity.com/paper/2608.02996/) (2026)
- [Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty](https://mlsanity.com/paper/2609.03460/) (2026)

## Abstract (arXiv)

This paper investigates how generative AI can potentially undermine the integrity of collective knowledge and the processes we rely on to acquire, assess, and trust information, posing a significant threat to our knowledge ecosystem and democratic discourse. Grounded in social and political philosophy, we introduce the concept of generative algorithmic epistemic injustice. We identify four key dimensions of this phenomenon: amplified and manipulative testimonial injustice, along with hermeneutical ignorance and access injustice. We illustrate each dimension with real-world examples that reveal how generative AI can produce or amplify misinformation, perpetuate representational harm, and create epistemic inequities, particularly in multilingual contexts. By highlighting these injustices, we aim to inform the development of epistemically just generative AI systems, proposing strategies for resistance, system design principles, and two approaches that leverage generative AI to foster a more equitable information ecosystem, thereby safeguarding democratic values and the integrity of knowledge production.