Gerhard Wagner

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Essay
Causal AI—A VISOR for the Law of Torts
Gerhard Wagner
Dr. Gerhard Wagner is the Chair of Civil Law, Commercial Law, and Law and Economics at Humboldt University of Berlin.

He has previously served as visiting professor at University College London, the University of Chicago, and Université Paris-Panthéon-Assas, as well as a visiting scholar at the New York University School of Law. His research focuses include torts, private law theory, and dispute resolution.

Causal AI is within reach. It has the potential to trigger nothing less than a conceptual revolution in the law. This Essay explains why and takes a cautious look into the crystal ball. Causation is an elusive concept in many disciplines—not only the law, but also science and statistics. Even the most up-to-date artificial intelligence systems do not “understand” causation, as they remain limited to the analysis of text and images. It is a long-standing statistical axiom that it is impossible to infer causation from the correlation of variables in datasets. This thwarts the extraction of causal relations from observational data. But important advances in computer science will enable us to distinguish between mere correlation and factual causation. At the same time, artificially intelligent systems are beginning to learn how to “think causally.”

Machine Learning Technology Regulation Torts
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86.2
Down by Algorithms? Siphoning Rents, Exploiting Biases, and Shaping Preferences: Regulating the Dark Side of Personalized Transactions
Gerhard Wagner
Chair for Private Law, Business Law, and Law and Economics at the Humboldt University in Berlin and Academic Director of Humboldt’s LLM program on International Dispute Resolution
Horst Eidenmüller
Freshfields Professor of Commercial Law at the University of Oxford and Professorial Fellow of St. Hugh’s College, Oxford.

The rise of big data and artificial intelligence creates novel and unique opportunities for business to consumer (B2C) transactions. Businesses assemble or otherwise gain access to comprehensive sets of data on consumer preferences, behavior, and resources.

Volumes