For the Public Good?
Values and Accountability in AI and Data Science
7:30 - 9:00 pm
1 November 2018
The Seattle Public Library, Microsoft Auditorium
1000 Fourth Ave, Seattle, WA 98104
Click here for directions
Artificial intelligence (AI) and data-intensive science are influencing all aspects of our lives. Our smart phones and search engines anticipate our needs and preferences, driverless cars and autonomous military weapons are no longer the stuff of SciFi, and life-changing judgments about everything from medical diagnoses and credit ratings to college admissions and parole decisions are informed by algorithm-driven data analysis. Yet questions about which data sets to mine, how a particular algorithm is constructed, and what kind of transparency we can demand for these powerful technologies persist. The PSA invites the public to join us in exploring these important issues:
What assumptions are built into the algorithms that make data mining and AI possible? How should developers change their practice to address encoded values? And ultimately, what “public good” should data science and AI serve?Panelists
Heather Douglas (Associate Professor of Philosophy, Michigan State University) Bases for Trust in AI Eric Horvitz (Technical Fellow and Director of Microsoft Research Labs) AI, People, and Society: Rising Questions and Directions Sabina Leonelli (Professor of Sociology, Philosophy and Anthropolo ... Seattle Public Library: Microsoft Auditorium PSA2018: The 26th Biennial Meeting of the Philosophy of Science Association office@philsci.orgFor the Public Good?
Values and Accountability in AI and Data Science
7:30 - 9:00 pm
1 November 2018
The Seattle Public Library, Microsoft Auditorium
1000 Fourth Ave, Seattle, WA 98104
Click here for directions
Artificial intelligence (AI) and data-intensive science are influencing all aspects of our lives. Our smart phones and search engines anticipate our needs and preferences, driverless cars and autonomous military weapons are no longer the stuff of SciFi, and life-changing judgments about everything from medical diagnoses and credit ratings to college admissions and parole decisions are informed by algorithm-driven data analysis. Yet questions about which data sets to mine, how a particular algorithm is constructed, and what kind of transparency we can demand for these powerful technologies persist. The PSA invites the public to join us in exploring these important issues:
Panelists
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