Should We Attend More or Less? Modulating Attention for Fairness

Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian, Sarath Chandar

Research Area: Societal implications

Keywords: attention modulation, bias, fairness, intra-processing, language models, attention, gender, race, religion, sexual orientation

TL;DR: This paper presents a novel approach to improve fairness in language models using attention modulation.

Abstract: The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of tasks, it also poses the risk of models learning harmful biases from the data, such as gender stereotypes. In this work, we investigate the role of attention, a widely-used technique in current state-of-the-art NLP models, in the propagation of social biases. Specifically, we study the relationship between the entropy of the attention distribution and the model's performance and fairness. We then propose a novel method for modulating attention weights to improve model fairness after training. Since our method is only applied post-training and pre-inference, it is an intra-processing method and is, therefore, less computationally expensive than existing in-processing and pre-processing approaches. Our results show an increase in fairness and minimal performance loss on different text classification and generation tasks using language models of varying sizes.

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Submission Number: 222