2026-2027 Law and Business Entrepreneurship Fellow
Haggai Porat
Haggai Porat is a Jacobson Fellow at NYU School of Law.Haggai’s research lies at the intersection of consumer law, privacy and data protection, and the regulation of AI-enabled markets, with a focus on algorithmic price discrimination and personalized pricing in digital commerce. A second strand of his research examines judicial decision-making and litigation. He combines empirical, computational, and theoretical methods in his work. His scholarship has appeared or is forthcoming in legal and interdisciplinary outlets, including the Journal of Legal Studies, the American Law and Economics Review, PLOS ONE, and The Cambridge Handbook on Price Personalization and the Law. Haggai is also an SJD candidate at Harvard Law School and a PhD candidate in Economics at Tel Aviv University, and he previously clerked at the Supreme Court of Israel.
Education
- SJD (Law), Harvard Law School, pending
- PhD (Economics), Tel Aviv University School of Economics, pending
- LLM (Law), Harvard Law School (requirements fulfilled, degree waived), 2020
- MA (Economics), Tel Aviv University School of Economics, magna cum laude, 2020
- LLB (Law), Tel Aviv University Faculty of Law, summa cum laude, 2016
- BA (Economics), Tel Aviv University School of Economics, summa cum laude, 2016
Publications
- Personalized Pricing by LLMs: Experimental Evidence of the Limit of Privacy Regulation, (work in progress) (with Rawan Qadri, Niva Elkin-Koren and Inbal Yahav)
- Bargaining with Algorithms: An Experiment on Algorithmic Price Discrimination and Consumer and Data Protection Laws American Law and Economics Review (forthcoming)
- Behavior-Based Price Discrimination and Data Protection in the Age of Algorithms, 55(1) The Journal of Legal Studies 129 (2026)
- Algorithmic Personalized Pricing in the United States: A Legal Void, The Cambridge Handbook on Price Personalization and the Law (Fabrizio Esposito & Mateusz Grochowski, eds.) 300 (2025)
- LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models, 36 NeurIPS Proceedings (2023) (with Neel Guha et al.)
- Are All Types of Discrimination Created Equal?, 47 Law & Psychology Review 1 (2023) (with Tamar Kricheli-Katz and Yuval Feldman)
Those Who Tan and Those Who Don't: A Natural Experiment on Colorism, 15 PLoS ONE 1 (2020) (with Tamar Kricheli-Katz, Tali Regev, Shay Lavie and Ronen Avraham)
Alessandro Tacconelli
Alessandro Tacconelli is a Jacobson Fellow at NYU School of Law and a doctoral candidate at the Center for Law and Economics at ETH Zurich. His research lies at the intersection of law, behavioral science, economics, and artificial intelligence. Using experimental, computational, and doctrinal methods, he studies how individuals and artificial agents respond to legal rules, how emerging technologies affect legal decision-making and standards of liability, and how the law can govern increasingly autonomous AI systems, with a particular focus on machine crime and the legal treatment of harmful conduct by artificial agents.
Before beginning his doctoral studies, Alessandro earned a Master of Behavioral and Decision Sciences from the University of Pennsylvania and a law degree, with highest honors, from Bocconi University. He previously worked as a legal associate at PwC Italy, advising clients on M&A and other corporate transactions. He also advises technology start-up projects through the University of Zurich’s Digital Society Initiative and has co-taught courses in contract design, social norms, business strategy and corporate governance, and experimental methods.
Education
- ETH Zurich, Center for Law and Economics, Doctoral Candidate, 2022–Present
- University of Pennsylvania, Master of Behavioral and Decision Sciences, 2022
- Bocconi University School of Law, Law Degree, cum laude, 2018
Publications
- Aczel, B. Szaszi…A. Tacconelli…B.A. Nosek, “Investigating the Analytical Robustness of the Social and Behavioral Sciences,” Nature 652, 135–142 (2026)
- A. Tacconelli, J. Merane, A. Nielsen, K. Tobia, B. Hackanson, A. Stremitzer, “How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial,” Journal of Nuclear Medicine (2026)
Works in Progress
- Foundations of Machine Criminology: A Law-and-Economics Case for a New Field.
- FRAUDBENCH: A Legal Evaluation of AI Deception” (with K. Wei, et al.).
- Criminal Machine Behavior: A Benchmark” (with I. Schlag et al.).
- Framing Rules: Rights by Permission and Rights by Exemption” (with A. Stremitzer and A. Leibovitch).