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个人简介
My thesis research is on recognizing and mitigating gender bias in natural language processing. AI models can amplify bias that exists in real-world data. For instance, if the majority of Wikipedia pages for scientists are male, the model will learn that male pronouns and names are more related to the word “scientist” than female pronouns and names.
Later, when a search engine uses this model and someone searches for famous scientists, pages containing male pronouns are more likely to be returned (even if all else were equal). In this way, the model helps reinforce a stereotype. When designing models with mitigation in mind, we attempt to correct for this amplification.
I’m also interested in learning about practical challenges in adopting research from Vector’s industry partners. For example, if we develop a new bias mitigation technique in our research, how can we disseminate that result in a way that will facilitate widespread adoption?
研究兴趣
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GEBNLP 2021: THE 3RD WORKSHOP ON GENDER BIAS IN NATURAL LANGUAGE PROCESSING (2021): 103-111
EDULEARN21 Proceedingspp.8224-8232, (2021)
ICERI ProceedingsICERI2020 Proceedings (2020)
ICERI ProceedingsICERI2020 Proceedings (2020)
Proceedings of the Western Canadian Conference on Computing Education (2019)
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