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AI in Public Sector: Tool for inclusion or exclusion?
Applications of Machine Learning (ML) and Artificial Intelligence (AI) in the public sector are broad and growing, helping make decisions in welfare payments, immigration, fraud detection, healthcare, transportation, etc. However, the social equity and diversity implications of ML & AI in this realm are unclear. The panel will discuss the potential social and economic impact and ethical considerations of ML & AI in the public realm. Moderator: Ed Lazowska, Profess, Paul G. Allen School Panelists: Emily Bender, Professor, UW Linguistics Shankar Narayan, Tech and Liberty Project Director, ACLU of WA Oren Etzioni, CEO, AI2 Emily Keller, Program Manager, Urbanalytics, i School Ryan Calo, Associate Professor, UW Law School Michael Phillips, Associate General Counsel Microsoft
AI Now: Social and Political Questions for Artificial Intelligence | Kate Crawford
On March 6th, Kate Crawford gave the Tech Policy Lab’s Spring Distinguished Lecture on “AI Now: Social and Political Questions for Artificial Intelligence.” The impact of early AI systems is already being felt, bringing with it challenges and opportunities, and laying the foundation on which future advances in AI will be integrated into social and political domains. The potential wide-ranging impact makes it necessary to look carefully at the ways in which these technologies are being applied now, whom they’re benefiting, and how they’re structuring our social, economic, and interpersonal lives. Kate Crawford is the co-founder (with Meredith Whittaker) of the AI Now Institute, a New York-based research center working across disciplines to understand the social and economic implications of artificial intelligence. She is a principal researcher at Microsoft Research New York City, a visiting professor at MIT’s Center for Civic Media, and a senior fellow at NYU’s Information Law Institute. Her research addresses the social implications of large scale data, machine learning and AI. Recent publications address the topics of data discrimination, social impacts of artificial intelligence, predictive analytics and due process, ethical review for data science, and algorithmic accountability.
