Summary of Debiasing Text Safety Classifiers Through a Fairness-aware Ensemble, by Olivia Sturman et al.
Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble
by Olivia Sturman, Aparna Joshi, Bhaktipriya Radharapu, Piyush Kumar, Renee Shelby
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents a lightweight post-processing method for mitigating counterfactual fairness in closed-source text safety classifiers, which learn societal biases when trained on imbalanced data. The approach involves building an ensemble that outperforms input classifiers, policy-aligns them, and acts as a debiasing regularizer. The authors introduce two threshold-agnostic metrics to assess counterfactual fairness and demonstrate how combining these metrics with Fair Data Reweighting (FDW) helps mitigate biases. The paper creates expanded Open AI datasets and a new templated LLM-generated dataset based on user-prompts, both of which are counterfactually balanced across identity groups and cover four key areas of safety. The results show that the approach improves counterfactual fairness with minimal impact on model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure language models don’t learn bad habits from the data they’re trained on. When we train these models, we need to make sure they’re not picking up biases and stereotypes. The authors have a new way of doing this that works well even when the data is unfair or biased. They created special datasets with balanced information across different groups and tested their method to see if it really works. It does! Their approach makes the models safer and more fair, which is important for many applications. |
