Due to the pandemic, this workshop will be available via Zoom Webinar. The workshop is open to the Caltech community.
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, I discuss 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 my own life, education and current work, I discuss how this knowledge hierarchy limits the field and potential ways forward.
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About the SpeakerTimnit Gebru is the cofounder of Black in AI, a place for sharing ideas, fostering collaborations and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence. She is the former co-lead of the Ethical Artificial Intelligence research team at Google. Timnit earned her doctorate under the supervision of Fei-Fei Li at Stanford University in 2017, and did a postdoc at Microsoft Research NYC in the FATE (Fairness, Accountability, Transparency, and Ethics in AI) team.
About the SeriesThe Diverse Minds Seminar Series is a campus-wide program that aims to provide a platform for distinguished speakers from a diversity of backgrounds to share their science and personal journey into science with the Caltech Community. These seminars aim to educate, inspire, and initiate purposeful discussion. Caltech recognizes that diverse perspectives and experiences benefit everyone and by intentionally inviting speakers from underrepresented and underserved backgrounds Caltech can continue to cultivate a community that values equity, inclusion, and embraces the power of diversity in STEM.