Description
This session will explore the “last-mile problem” of data science where algorithms can, but may not always, point us in the right direction. Data science rarely ever becomes the complete solution. Judgment and decision-making follow. Whether we have the appropriate models and algorithms in the era of “big data” or not, we are all still human beings with our respective psychologies, experiences and human biases. Some have voiced fears that artificial intelligence could replace humans altogether. But is that likely?
A more valuable approach may be to view machine and human intelligence as complementary, with each bringing its own strengths to the table.
Join us in an interactive session with the audience in discussing some of these questions and explore how data science and behavioral science can work together in our continued evolution and collaboration of artificial intelligence and human decision-making.
Contributors
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David Ingram
Dave is now a writer and independent researcher on risk and risk management. Prior to retirement he led Willis Re’s ERM Advisory group. Dave was previously in the Insurance Ratings Group of Standard and Poor's where he led their initiative to incorporate ERM into insurance ratings.
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James Guszcza
James Guszcza is a 2020-21 fellow at Stanford’s Center for Advanced Study in the Behavioral Sciences. Jim has worked as data scientist for two decades and is the first person to be designated Deloitte’s U.S. Chief Data Scientist, is a former professor at the University of Wisconsin-Madison business school, and holds a PhD in philosophy from The University of Chicago. He serves on the scientific advisory board of the Psychology of Technology Institute.
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Kudakwashe Chibanda
Kuda is a Director of Data Science at The Hartford, where she leads teams of cross-disciplinary data scientists who consult with business functions to build solutions across the insurance value chain. She serves on the board of the CAS