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A.I.’s HUMAN SHADOW: Bias and Error in our Algorithmic Selves

Sunday, June 3, 2018
1:30 pm - 3:00 pm EST

Whether we like it or not, whether we realize it or not, A.I. is now embedded in our everyday existence. From healthcare to law enforcement, from driverless cars to autopilots in flight, from e-commerce to employment applications–all rely on A.I. to make crucial decisions that have far-reaching implications to our bodies, our wallets, our safety, and our lives. So how good is A.I. at doing its job? Only as good as the data it’s given, and that data is often tainted by human bias and/or error. Can these biases and errors be eliminated? Are we damaging the fabric of society by handing so much power to A.I., or saving it? Join our salon of scientists and ethicists as they grapple with these and a host of issues that will define our future.

The Big Ideas Series is supported in part by the John Templeton Foundation.


Solon BarocasEthicist

Solon Barocas is an Assistant Professor in the Department of Information Science at Cornell University. His current research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference.

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Timnit GebruComputer Scientist

Timnit Gebru works in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research, New York. She is currently studying the ethical considerations underlying any data mining project, and methods of auditing and mitigating bias in sociotechnical systems.

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Jeanna MatthewsComputer Scientist

Jeanna N. Matthews is an Associate Professor of Computer Science at Clarkson University in Potsdam, New York and a 2017-18 Fellow at Data and Society. She is a co-chair of the Association for Computing Machinery Subcommittee of Algorithmic Transparency and Accountability and an ACM Distinguished Speaker.

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Clifford SteinComputer Scientist

Clifford Stein is Professor of Industrial Engineering and Operations Research and of Computer Science at Columbia University. He is also a member of the Data Science Institute.

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