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In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny in recent years. In this talk the role datasets play in model development and in the broader social organization of the field will be examined. A host of concerns that have been identified relating to the dominant practices of dataset development and used across the field, as well as the strengths and deficiencies of interventions that have emerged in response to these concerns will be summarized.
Bio: Emily Denton (they/them) is a Senior Research Scientist at Google, studying the societal impacts of artificial intelligence (AI) technology and the conditions of AI development. Prior to joining Google, Denton received their Ph.D. in machine learning from the Courant Institute of Mathematical Sciences at New York University, focusing on unsupervised learning and generative modeling of images and video.
Though trained formally as a computer scientist, Denton draws ideas and methods from multiple disciplines and is drawn towards highly interdisciplinary collaborations, in order to examine AI systems from a sociotechnical perspective. Their recent research centers on a critical examination of the histories of datasets — and the norms, values, and work practices that structure their development and use — that make up the underlying infrastructure of AI research and development.
Denton is queer and nonbinary and uses they/them pronouns. They are also a circus aerialist, rock climber, and cat parent of two.
To request accommodations for a disability please contact Jean Butcher, butcher@princeton.edu, at least one week prior to the event.
This seminar will be recorded.