Data, Transparency, and AI Ethics

The interdisciplinary field of AI ethics has started new debates into the fairness of particular algorithms and the role of algorithms in automated decision making systems. In the first part of this talk, I introduce the reporting and transparency work that Google’s Ethical AI team has been pursuing around the models and data involved in these systems. A major assumption of this work is the stability of particular ontologies of socially salient characteristics. In the second part of this talk, I turn to critical race theory and sociological work on race and ethnicity to ground conceptualizations of race for algorithmic fairness and machine learning more broadly. Lastly, I outline a research program around the genealogy of data used in machine learning research. While machine learning has seen a rapid proliferation of new methods, the datasets which undergird these methods have received comparatively little attention. A research program around the genealogy of these datasets should be attentive to the constellation of organizations and stakeholders involved in their creation, the intent, values, and assumptions of their authors and curators, and the adoption of datasets by subsequent researchers.

This is an online event. It will be live streamed on the Centre for Ethics YouTube Channel on Tuesday, September 22. Channel subscribers will receive a notification at the start of the live stream. (For other events in the series, and to subscribe, visit YouTube.com/c/CentreforEthics.)

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Alex Hanna
ML Fairness
Google

Tue, Sep 22, 2020
04:00 PM - 05:30 PM
Centre for Ethics, University of Toronto
200 Larkin