Vasant Dhar

Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at New York University,[1] former Editor-in-Chief of the journal Big Data.[2] and the founder of SCT Capital, one of the first machine-learning-based hedge funds in New York City in the 90s.

Vasant Dhar
Vasant Dhar, Former Editor-in-Chief, Big Data Journal; Professor, Stern School of Business; Faculty, NYU Center for Data Science
Nationality	India
Alma mater	University of Pittsburgh Indian Institute of Technology Delhi The Lawrence School Sanawar
Scientific career
Fields	Data science Information Systems Machine Learning Artificial Intelligence Big Data Finance
Institutions	New York University

Dhar is an Artificial Intelligence researcher and data scientist whose research addresses the following question: when do we trust AI systems with decision making? The question is particularly relevant to current-day autonomous machine-learning-based systems that learn and adapt with ongoing data.

Dhar's research has been motivated by a building predictive models in a number of domains, most notably finance, as well as areas including healthcare, sports, education, and business. Why are we willing to trust machines in some areas and not others?

Dhar's view is that there is a discontinuity when we give complete decision-making control to a machine that learns from ongoing data. This discontinuity introduces some risks, specifically those around the errors made by such systems, which directly impact our degree of trust in them.

Dhar's research breaks down trust along 2 risk-based dimensions: predictability, or how frequently a system makes mistakes (X-axis) and the associated costs of error (Y-axis) of such mistakes. The research demonstrates the existence of an “automation frontier” that expresses a tradeoff between how often a system will be wrong and the consequences of such mistakes. Trust, and hence our willingness to cede control of decision making to the machine, increases with increasing predictability and lower error costs. In other words, we are willing to trust machines if they don't make too many mistakes and their costs are tolerable. As mistakes increase, we require that their consequences be less costly.

The automation frontier provides a natural way to think about the future of work. With more and better data and algorithms, parts of existing processes become automated due to increased predictability, and cross the automation frontier into the “trust the machine” zone, whereas the parts with high error costs remain under human control. The model provides a way to think about the changing responsibilities of humans and machines as more data and better algorithms become better than humans with decisions.

Dhar also uses the framework to frame policy issues around the risks of AI-based social media platforms and issues of privacy and ethical uses and governance of data. He writes regularly in the media on Artificial Intelligence, societal risks of AI platforms, data governance, privacy, ethics, and trust. He is a frequent speaker in academic as well as industrial forums.

Professor Dhar teaches courses on Systematic Investing, Prediction, Data Science, and Foundations of FinTech. He has written over 100 research articles, funded by grants from industry and government agencies such as the National Science Foundation.

Professor Dhar received his Bachelor of Technology from the Indian Institute of Technology in Delhi, and his Master of Philosophy and Doctor of Philosophy from the University of Pittsburgh.

He is a graduate of the Lawrence School Sanawar, which he considers one of the best presents his parents gave him without realizing it.