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Talk Title: The moral salience of the algorithmic lens
Our world today is filtered through the algorithmic lens -- a vast assemblage of algorithmic tools and data that mediate our experience of life, work, and health. This lens reveals and distorts: it forces the world into forms that are machine-readable, and in doing so renders some aspects sharper, and makes others invisible.
The algorithmic lens requires precision, and data. It seeks to optimize, and does so at scale. But to render ourselves amenable to the algorithmic lens, we make choices -- choices that implicitly encode values and judgements. Deploying an algorithm starts a moral conversation you didn't know you were having.
In this talk, I will unpack the elements of the algorithmic lens, and seek to explore its limits. I'll argue why it is important that we recognize the existence of the lens and study its limits carefully. It is only in doing so that we can begin to understand how to design systems that align with our goals and values
Suresh Venkatasubramanian directs the Center for Technological Responsibility, Reimagination, and Redesign (CNTR) with the Data Science Institute at Brown University, and is a Professor of Computer Science and Data Science. Suresh's background is as a computer scientist and his current research interests lie in algorithmic fairness, and more generally the impact of automated decision-making systems in society.
Suresh recently finished a stint in the Biden-Harris administration, where he served as Assistant Director for Science and Justice in the White House Office of Science and Technology Policy. In that capacity, he helped co-author the Blueprint for an AI BIll of Rights.
Prior to Brown University, Suresh was at the University of Utah, where as an assistant professor he was the John and Marva Warnock Assistant Professor. He has received a CAREER award from the NSF for his work in the geometry of probability, a test-of-time award at ICDE 2017 for his work in privacy, and a KAIS Journal award for his work on auditing black-box models. His research on algorithmic fairness has received press coverage across the globe, including NPR’s Science Friday, NBC, and CNN, as well as in other media outlets. He is a past member of the Computing Community Consortium Council of the CRA, spent 4 years (2017-2021) as a member of the board of the ACLU in Utah, and is a past member of New York City’s Failure to Appear Tool (FTA) Research Advisory Council, the Research Advisory Council for the First Judicial District of Pennsylvania and the Utah State Auditor's Commission on protecting privacy and preventing discrimination. He was recently named by Fast Company to their AI20 list of thinkers shaping the world of generative AI.
Please register here by the end of the day on Thursday, February 8 to be included for lunch.