Session Overview

Natural language processing (NLP) has made truly impressive progress in recent years and is being deployed in an ever-increasing range of user-facing settings. Accompanied by this progress has been a growing realization of inequities in the performance of naively-trained NLP models for users of different demographics, with minorities typically experiencing lower performance levels. In this talk, I will illustrate the nature and magnitude of the problem, and outline a number of approaches that can be used to train fairer models based on different data settings, without sacrificing overall performance levels. The talk will assume intermediate familiarity with NLP and machine learning methods and is relevant to all industries.


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    Fairness in Natural Language Processing

    • Abstract & Bio

    • Fairness in Natural Language Processing


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