Title: GRADIEND: Feature Learning within Neural Networks Exemplified through Biases
Primary area: interpretability and explainable AI
Keywords: Feature Learning, Bias Mitigation, AI Fairness, Language Models

Abstract: AI systems frequently exhibit and amplify social biases, leading to harmful consequences in critical areas. This study introduces a novel encoder-decoder approach that leverages model gradients to learn a feature neuron encoding societal bias information such as gender, race, and religion. We show that our method can not only identify which weights of a model need to be changed to modify a feature, but even demonstrate that this can be used to rewrite models to debias them while maintaining other capabilities. We demonstrate the effectiveness of our approach across various model architectures and highlight its potential for broader applications.

TL;DR: Using model gradients, we learn a single feature neuron that encodes a desired feature along an orthogonal axis (e.g., gender) and show that this can debias models.