Promoting Machine Learning Fairness Education through Active Learning and Reflective Practices

Abstract

As Natural Language Processing (NLP) has witnessed significant progress in the last decade and language technologies have gained widespread usage, there is an increasing acknowledgement that the choices made by NLP researchers and practitioners regarding data, methods, and tools carry significant ethical and societal implications. Consequently, there arises a pressing need for integrating ethics education into computer science (CS) curriculum, specifically within NLP and other related machine learning (ML) courses. In this project, our primary objective was to highlight the importance of fairness in ML ethics. We aimed to raise awareness regarding biases that can exist in machine learning, such as gender bias and disability bias. Acknowledging the intricate nature of the intersection between machine learning, ethics, and bias, we formed a participatory group comprising professors and students to develop the teaching interventions. The group members have experiences in machine learning, accessible computing, or both. It was crucial to include students in the design process of the teaching interventions because we wanted to ensure that fairness is sufficiently covered without being too complex to understand or too subtle to recognize [Tseng et al., 2022].

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