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Keynote: "Incremental AI"

Speaker: Professor Daniel L. Chen, Director of Research at the Centre National de la Recherche Scientifique (CNRS) and Professor at the Toulouse School of Economics.


The usual narrative is backlash to AI. A recent study found that in Kentucky, when judges were given decision-support, it ended up increasing disparities – not because the algorithm was biased – in fact the algorithm would have resulted in lower disparities. But the judges selectively paid attention to the algorithm, which resulted in greater disparities.

I’m going to argue for an incremental approach leveraging recent theoretical insights from social preference economics. The core insight is that judges are moral decision-makers, you’re right or wrong, good or bad, and to understand what motivates these decision-makers, one might turn to self-image motives – "I think I’m a good person – a good judge" – a topic of active research in recent years. Each stage leverages motives of self-image, self-improvement, self-understanding, and ego. In stage 1, people use AI as a support tool, speeding up existing processes (for example, by prefilling forms). Once they’re used to this, they can more easily accept an added functionality (Stage 2) in which AI becomes a choice monitor, pointing out choice inconsistencies and reminding the human of her prior choices in similar situations. Stage 3 elevates the AI to the role of a more general coach, providing outcome feedback on choices and highlighting decision patterns. Then, in stage 4, the AI brings in other people’s decision histories and patterns, serving as a platform for a community of experts.

Short bio for Daniel L. Chen

Daniel L. Chen

Daniel L. Chen is Director of Research at the Centre National de la Recherche Scientifique, CNRS, and Professor at the Toulouse School of Economics. He is also a Senior Fellow at the Institute for Advanced Study in Toulouse, Collaborator at Harvard Medical School, Advisor for NYU Courant Institute for Mathematics Center for Data Science, and founder of oTree Open Source Research Foundation and Data Science Justice Collaboratory. Chen was previously Chair of Law and Economics and tenure-track assistant professor in Law (primary), Economics, and Public Policy at Duke University.

Chen received his BA and MS (summa cum laude) from Harvard University in Applied Mathematics and Economics (thesis advisor: Michael Kremer, 2019 Nobel Prize winner); completed his Economics PhD from MIT (thesis advisor: Esther Duflo, 2019 Nobel Prize winner and a trailblazer in utilizing new methods to improve decision making for the field of development economics, which Chen is doing for law); and obtained a JD from Harvard Law School. Chen uses his extensive empirical training to tackle longstanding legal questions previously difficult to empirically analyze. He has attained prominence through the development of open source tools to study human behavior and through large-scale empirical studies – data science, artificial intelligence, and machine learning – on the relationship between law, social norms and the enforcement of legal norms, and on judicial systems.

Page Manager: Catharina Jerkbrant|Last update: 11/5/2019

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