[OFF] Palestra em disparidades de genero em citacoes (em NLP e outras areas da ciencia)
valeria.depaiva
Women+@DCS Seminar Series, University of Sheffield
Title: Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations
Invited Speaker: Saif M. Mohammad, National Research Council (Canada)
Date and Time: October 28, 2pm (GMT)
Online Access link: https://eu.bbcollab.com/guest/0bb3c20a99f24543898320e4da1092a4
*Access link will open 30 minutes prior to the seminar
Short Bio:
Dr. Saif M. Mohammad is Senior Research Scientist at the National Research Council Canada (NRC). He received his Ph.D. in Computer Science from the University of Toronto. Before joining NRC, he was a Research Associate at the Institute of Advanced Computer Studies at the University of Maryland, College Park. His research interests are in Computational Linguistics and Natural Language Processing (NLP), especially Lexical Semantics, Emotions in Language, Sentiment Analysis, Computational Creativity, Fairness in NLP, Psycholinguistics, and Information Visualization. He has served in various capacities at prominent journals and conferences, including: action editor for Computational Linguistics, chair of the Canada--UK symposium on Ethics in AI, co-chair of SemEval 2017-19 (the largest platform for semantic evaluations), workshops co-chair for ACL 2020, co-organizer of WASSA 2017 and 2018 (a sentiment analysis workshop), and area chair for ACL, NAACL, and EMNLP (for sentiment analysis, lexical semantics, and fairness in NLP). His word--emotion resources, such as the NRC Emotion Lexicon, are widely used for analyzing affect in text. His work has garnered media attention, including articles in Time, SlashDot, LiveScience, io9, The Physics arXiv Blog, PC World, and Popular Science.
Abstract:
Disparities in authorship and citations across gender can have substantial adverse consequences not just on the disadvantaged genders, but also on the field of study as a whole. Measuring gender gaps is a crucial step towards addressing them. In this work, we examine female first author percentages and the citations to their papers in Natural Language Processing (1965 to 2019). We determine aggregate-level statistics using existing manually curated author--gender lists as well as first names strongly associated with a gender. We find that only about 29% of first authors are female and only about 25% of last authors are female. Notably, this percentage has not improved since the mid 2000s. We also show that, on average, female first authors are cited less than male first authors, even when controlling for experience and area of research. Finally, we discuss the ethical consideration involved in automatic demographic analysis.