Summary of Building Socially-equitable Public Models, by Yejia Liu et al.
Building Socially-Equitable Public Models
by Yejia Liu, Jianyi Yang, Pengfei Li, Tongxin Li, Shaolei Ren
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 research paper proposes a novel approach to training public models for use in various AI applications. The authors recognize that current public models are optimized solely for prediction accuracy, which may not align with the diverse objectives of downstream agents using these predictions. To address this issue, they introduce an Equitable Objective that integrates the objectives of downstream agents into the optimization process. This approach aims to produce a more equitable performance distribution across diverse downstream agents, each with their unique concerns. The authors demonstrate the effectiveness of their method through both theoretical analysis and empirical case studies. Their work has the potential to advance performance equity in AI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making sure that public models, which are used by many different things like chatbots or recommendation systems, don’t unfairly favor one group over another. Right now, these models are only optimized for being accurate, but that doesn’t take into account the different goals of the things using them. The authors suggest a new way to train these models so they work more fairly. They show that their method works by testing it and comparing it to other methods. |
Keywords
» Artificial intelligence » Optimization
