All 100 prisoners in a prison participate in a riot, and 99 of them participate in attacking and killing a guard (the other returned to his cell briefly after the riot). With the guard dead, all 100 prisoners then escape. The next day one of the prisoners is captured and charged with participating in the murder of the guard. While admitting to participating in the riot the prisoner claims that he was the one who was not involved in attacking the guard. In the absence of any other evidence there is 99% probability the prisoner is guilty. Is this sufficient to convict?
Christian contrasts the above kind of naked statistical evidence with forensic evidence, such as a footprint found at a crime scene whose pattern 'matches' that of a shoe worn by the suspect. Whereas the causal link between the statistical evidence and guilt goes from the former to the latter, the causal link between the forensic evidence and guilt goes from the latter to the former:
recent paper about the 'opportunity prior' that we co-authored with Christian. The fact that the suspect was at the prison means that he had the 'opportunity' to participate in the killing and that the prior probability for guilt given the naked statistical evidence is 99%.
Christian talks about his latest paper, and at the end of the interview (24:50), he defends the Bayesian approach to legal evidence against attacks from some legal scholars (this is something we also did in our recent paper on countering the ‘probabilistic paradoxes in legal reasoning’ with Bayesian networks).
- Dahlman, C. (2019). "Naked Statistical Evidence and Incentives for Lawful Conduct ", https://www.researchgate.net/publication/336011753_Naked_Statistical_Evidence_and_Incentives_for_Lawful_Conduct
- Fenton, N. E., Lagnado, D. A., Dahlman, C., & Neil, M. (2019). "The Opportunity Prior: A proof-based prior for criminal cases", Law, Probability and Risk, DOI 10.1093/lpr/mgz007. Full paper from OUP. See also blog post
- de Zoete, J., Fenton, N. E., Noguchi, T., & Lagnado, D. A. (2019). "Countering the ‘probabilistic paradoxes in legal reasoning’ with Bayesian networks". Science & Justice 59 (4), 367-379 10.1016/j.scijus.2019.03.003 The pre-publication version (pdf) See also blog post.