Evidence Law in times of a “Big Data Revolution”
- Datum: 27.07.2023
- Uhrzeit: 17:00 - 19:00
- Vortragende: Prof. Liat Levanon, Ph.D. (Dickson Poon School of Law, King's College London)
- Liat Levanon is a Senior Lecturer in Evidence and Criminal Jurisprudence at the Dickson Poon School of Law, King’s College London. Before joining KCL, she was a Lecturer in Law at Brunel University London. She wrote her PhD dissertation at Cambridge University, and she was a Post-Doctoral Researcher at Zurich University. Before moving to the UK, Liat worked for the Israel Democracy Institute and taught at the Hebrew University of Jerusalem and at several other law schools in Israel.
- Video: mit Videopodcast (Link auf dieser Seite)
- Ort: Freiburg, Fürstenbergstr. 19
- Raum: Seminarraum (F 113) | Gäste sind herzlich eingeladen; Anmeldung erbeten
- Gastgeber: Max-Planck-Institut zur Erforschung von Kriminalität, Sicherheit und Recht
- Kontakt: email@example.com
The ‘Big Data Revolution’ has allowed accumulating significant amounts of statistical data covering all areas of life. Such data can have high probative value in legal trials. Still, courts in and outside the UK have been ambivalent about the possibility of making legal judgements based solely on statistics. The presentation will seek to uncover the root of this ambivalence, and to suggest that it highlights a fundamental connection between practical normativity and epistemic normativity. The argument will be that a Razian concept of respect for persons provides reasons against legal judgments of liability based solely on statistics. This is because statistics logically bar the elimination of the risk of error, and relatedly, they cannot justify belief in the availability of reasons to make a judgment of liability. The presentation will further seek to demonstrate that the argument carries over to non-legal spheres in which the Razian notion of respect plays a role. The UK’s attempt to determine A-level grades algorithmically during the Covid-19 pandemic provides an example.