Summary of Conditional Fairness For Generative Ais, by Chih-hong Cheng et al.
Conditional Fairness for Generative AIs
by Chih-Hong Cheng, Harald Ruess, Changshun Wu, Xingyu Zhao
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Logic in Computer Science (cs.LO); Software Engineering (cs.SE)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses significant fairness concerns in the deployment of generative AI (GenAI) models by introducing novel characterization and enforcement techniques tailored to GenAI’s broad functionality. The authors define two levels of fairness: output-based fairness, which evaluates generated outputs independently of prompts and models, and inherent fairness with neutral prompts. To address the complexity of GenAI and challenges in specifying fairness, the paper focuses on bounding the worst-case scenario by considering a GenAI system unfair if the distance between appearances of a specific group exceeds preset thresholds. The authors also explore combinatorial testing for assessing relative completeness in intersectional fairness. By bounding the worst case, the paper develops a prompt injection scheme within an agent-based framework to enforce conditional fairness with minimal intervention, validated on state-of-the-art GenAI systems. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative AI (GenAI) is changing the way we interact with technology, but it also raises important questions about fairness. This paper explores how to make sure GenAI models are fair and unbiased. The authors develop new techniques to ensure that GenAI models don’t favor certain groups or individuals over others. They show that fairness in GenAI requires a different approach than standard AI, because GenAI can generate a wide range of outputs. The authors also test their ideas on real-world GenAI systems. |
Keywords
» Artificial intelligence » Prompt