Summary of Why Don’t Prompt-based Fairness Metrics Correlate?, by Abdelrahman Zayed et al.
Why Don’t Prompt-Based Fairness Metrics Correlate?
by Abdelrahman Zayed, Goncalo Mordido, Ioana Baldini, Sarath Chandar
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 proposed paper investigates the potential biases learned by large language models and proposes a method to enhance the reliability of fairness assessment using prompts. The authors demonstrate that existing prompt-based fairness metrics exhibit poor agreement, which raises concerns about their effectiveness in evaluating and mitigating biases. To address this issue, they outline six reasons for the low correlation between metrics and introduce Correlated Fairness Output (CAIRO), a novel method to improve the correlation between fairness metrics. CAIRO uses pre-trained language models to augment original prompts and selects the combination that achieves the highest correlation across metrics. The authors show significant improvements in Pearson correlation for gender and religion biases, highlighting the potential of their approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how language models might learn biased information and proposes a way to make fairness assessments more reliable. They find that different ways of measuring fairness don’t agree well with each other, which is concerning because it means we can’t rely on these methods to fix biases. To understand why this is the case, they identify six key reasons for the poor agreement. Then, they develop a new method called Correlated Fairness Output (CAIRO) that improves the agreement between fairness metrics by using pre-trained language models and selecting the best prompts. The authors show that their approach can make a big difference in assessing biases related to gender and religion. |
Keywords
» Artificial intelligence » Prompt
