Summary of The Impossibility Of Fair Llms, by Jacy Anthis et al.
The Impossibility of Fair LLMs
by Jacy Anthis, Kristian Lum, Michael Ekstrand, Avi Feller, Alexander D’Amour, Chenhao Tan
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
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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 The paper discusses the importance of fairness in artificial intelligence (AI) applications, particularly with the rise of large language models like ChatGPT. It reviews existing technical frameworks for evaluating fairness, such as group fairness and fair representations, but finds that these approaches are limited when applied to large language models. The authors then propose guidelines for achieving fairness in specific use cases, emphasizing the importance of context, developer responsibility, and stakeholder participation. They also suggest that AI systems themselves may eventually be used to address fairness challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can make sure AI is fair. Right now, there are many ways to evaluate if an AI system is being unfair, like group fairness or fair representations. But these methods don’t work well for big language models that can understand lots of things. The authors think about this and come up with some rules for making AI more fair in specific situations. They also suggest that maybe one day AI itself could help us make sure it’s being fair. |
