Machine Learning and the Evaluation of Criminal Evidence | Alicia Carriquiry | WiDS 2019

Alicia Carriquiry Distinguished Professor and President’s Chair in Statistics | Director of CSAFE, Iowa State University

In the US criminal justice system, jurors choose between two competing hypothesis: the suspect is the source of the evidence found at the crime scene or s/he is not. The likelihood ratio framework, which relies on Bayes’ theorem for assessing the probative value of evidence, is difficult to implement in practice, when evidence is in the form of an image. Machine learning provides a good alternative for determining whether the evidence supports the proposition that the suspect may have been its source. We illustrate these ideas using information about the surface topography of bullet lands.