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Vasant Dhar 2018-09-07

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September 7, 2018

One South
Main Street
Room 120

10:30am – Noon


Professor of Information Systems and Center for Data Science,
New York University


Modern day AI machines learn and improve themselves based on ever-increasing amounts of data that humans and machines generate with each passing day. This happens largely through supervised learning, which is a major branch of Artificial Intelligence. But such machines also make mistakes.

In his talk, Dhar answers the question “when do we trust machines?” by walking us through various situations in our everyday lives — investing, playing sports, riding in driverless cars, and using social media platforms — to encourage us to question the faith we put in technology. In answer to his question, Dhar utilizes a “trust heatmap” in order to illustrate how the answer depends on two key elements: how often machines make mistakes and the costs or consequences of these mistakes. He proposes how the heatmap can be used to inform executives in prioritizing data science initiatives, and how policy makers should evaluate the risks associated with different policies of data use.


Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at New York University. He is former Editor-in-Chief of the journal Big Data, and the founder of SCT Capital Management, a machine-learning-based hedge fund in New York City. Dhar’s central research question asks when we should trust AI machines that learn from data. His research has addressed this question in a number of areas including financial markets, social media and healthcare. Dhar has authored over 100 research papers, as well as articles for publications such as the Financial Times, Wall Street Journal, Forbes, Wired, and the Harvard Business Review. He has appeared on CNBC, Bloomberg TV, and National Public Radio. His recent TEDx talk, “When do we trust machines?” is available at: https://www.youtube.com/ watch?v=dO9D6l_THhk

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