Fairness in Natural Language Processing


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 realisation 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.


Tim Baldwin is a Melbourne Laureate Professor in the School of Computing and Information Systems, The University of Melbourne, and also Director of the ARC Centre for Cognitive Computing in Medical Technologies and Vice President of the Association for Computational Linguistics. His primary research focus is on natural language processing (NLP), including social media analytics, deep learning, and computational social science.

Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of Technology in 1998 and 2001, respectively. Prior to joining The University of Melbourne in 2004, he was a Senior Research Engineer at the Center for the Study of Language and Information, Stanford University (2001-2004). His research has been funded by organisations including the Australia Research Council, Google, Microsoft, Xerox, ByteDance, SEEK, NTT, and Fujitsu, and has been featured in MIT Tech Review, IEEE Spectrum, The Times, ABC News, The Age/SMH, Australian Financial Review, and The Australian. He is the author of well over 400 peer-reviewed publications across diverse topics in natural language processing and AI, with over 16,000 citations and an h-index of 60 (Google Scholar), in addition to being an IBM Fellow, ARC Future Fellow, and the recipient of a number of best paper awards at top conferences.

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