Summary of Epistemic Injustice in Generative Ai, by Jackie Kay et al.
Epistemic Injustice in Generative AI
by Jackie Kay, Atoosa Kasirzadeh, Shakir Mohamed
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential risks posed by generative AI in manipulating collective knowledge and trust in information. The concept of “generative algorithmic epistemic injustice” is introduced, highlighting four key dimensions: amplified testimonial injustice, hermeneutical ignorance, access injustice, and misinformation perpetuation. Real-world examples illustrate how generative AI can produce or amplify misinformation, create representational harm, and exacerbate epistemic inequities in multilingual contexts. The paper aims to inform the development of epistemically just generative AI systems by proposing strategies for resistance, system design principles, and two approaches that leverage generative AI to promote a more equitable information ecosystem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence can affect what we know and trust. The authors introduce a new idea called “generative algorithmic epistemic injustice” which shows how AI can spread misinformation, make some people’s voices unheard, and create unequal access to knowledge. They give examples of how this can happen in real-life situations. The goal is to make sure that AI systems are fair and help us get the information we need while keeping our democratic values safe. |
