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The hierarchy of knowledge in machine learning and related fields and its consequences
Feminist and race and gender scholars have long critiqued "the view from nowhere" that assumes science is "objective" and studied from no particular standpoint. In this talk, Gebru discusses how this view has resulted in a hierarchy of knowledge in machine learning and related fields, devaluing some types of work and knowledge (e.g. those related to data production, annotation and collection practices) and mostly amplifying specific types of contributions. This hierarchy also results in valuing contributions from some disciplines (e.g. Physics) more than others (e.g. race and gender studies). With examples from her own life, education and current work, Gebru shares how this knowledge hierarchy limits the field and potential ways forward.

Apr 14, 2021 12:00 PM in Eastern Time (US and Canada)

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