Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speech
This work addresses the problem of balancing fairness and accuracy in toxic language detection for NLP practitioners, though it is incremental in combining existing techniques.
The paper tackles the challenge of optimizing NLP models for both fairness and accuracy in toxic speech detection by developing a differentiable version of Accuracy Parity and using HyperNetwork optimization to learn Pareto-optimal trade-offs. The results demonstrate the method's effectiveness across two datasets, three neural architectures, and three fairness losses.
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.