Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble

TL;DR

This paper introduces a lightweight, post-processing ensemble method to reduce societal biases in text safety classifiers, improving fairness with minimal performance loss.

Contribution

It proposes a novel ensemble-based debiasing technique combined with counterfactual fairness metrics and new balanced datasets for safer language model outputs.

Findings

Enhanced counterfactual fairness in classifiers

Minimal impact on safety classifier performance

Effective bias mitigation using ensemble and FDW

Abstract

Increasing use of large language models (LLMs) demand performant guardrails to ensure the safety of inputs and outputs of LLMs. When these safeguards are trained on imbalanced data, they can learn the societal biases. We present a light-weight, post-processing method for mitigating counterfactual fairness in closed-source text safety classifiers. Our approach involves building an ensemble that not only outperforms the input classifiers and policy-aligns them, but also acts as a debiasing regularizer. We introduce two threshold-agnostic metrics to assess the counterfactual fairness of a model, and demonstrate how combining these metrics with Fair Data Reweighting (FDW) helps mitigate biases. We create an expanded Open AI dataset, 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; we will work towards publicly releasing these datasets. Our results show that our approach improves counterfactual fairness with minimal impact on model performance.