Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speech

TL;DR

This paper introduces a differentiable fairness measure and a HyperNetwork-based optimization method to efficiently find Pareto-optimal trade-offs between accuracy and fairness in toxic speech detection models.

Contribution

It proposes a novel differentiable fairness measure and a model-agnostic optimization approach to balance accuracy and fairness in NLP tasks.

Findings

Effective Pareto trade-offs achieved across multiple datasets and models.

Method generalizes well to different neural architectures and fairness losses.

Demonstrates improved fairness without sacrificing accuracy significantly.

Abstract

Optimizing NLP models for fairness poses many challenges. Lack of differentiable fairness measures prevents gradient-based loss training or requires surrogate losses that diverge from the true metric of interest. In addition, competing objectives (e.g., accuracy vs. fairness) often require making trade-offs based on stakeholder preferences, but stakeholders may not know their preferences before seeing system performance under different trade-off settings. To address these challenges, we begin by formulating a differentiable version of a popular fairness measure, Accuracy Parity, to provide balanced accuracy across demographic groups. Next, we show how model-agnostic, HyperNetwork optimization can efficiently train arbitrary NLP model architectures to learn Pareto-optimal trade-offs between competing metrics. Focusing on the task of toxic language detection, we show the generality and efficacy of our methods across two datasets, three neural architectures, and three fairness losses.