Papers with mitigating counterfactual fairness
Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble (2024.emnlp-industry)
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| Challenge: | Increasing use of large language models (LLMs) require performant guardrails to ensure the safety of inputs and outputs . when these guardrail are trained on imbalanced data, they can learn the societal biases resulting from the model's performance. |
| Approach: | They propose a method for mitigating counterfactual fairness in closed-source text safety classifiers by using a debiasing regularizer and a threshold-agnostic metric. |
| Outcome: | The proposed method outperforms classifiers and acts as a debiasing regularizer . it uses threshold-agnostic metrics and Fair Data Reweighting (FDW) to assess the counterfactual fairness of a model . |