Timnit 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 Transparency Accountability and Ethics in AI) team.
The hierarchy of knowledge in machine learning & related fields and its consequences
Feminist and race & 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.
Follow this link to view the recorded talk. Please note: This talk recording is available to Caltech personnel only. Caltech-access credentials are required to view. Broadband connection is recommended for optimum quality.
