Why Don't Prompt-Based Fairness Metrics Correlate?
This addresses reliability issues in fairness assessment for AI practitioners, but it is incremental as it builds on existing metrics.
The paper tackles the problem of poor correlation among prompt-based fairness metrics for large language models, proposing a method called CAIRO that significantly improves Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 for gender and religion biases, respectively.
The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.