Elon Law Professor Michael Rich’s article “Machines as Crime Fighters—Are You Ready?” in the winter 2016 edition of the American Bar Association’s Criminal Justice magazine, examines legal and practical challenges to the automated prediction of criminals.
“Over the last several years, a convergence of machine learning applications and the real-time identification of street criminals has been creeping ever closer to reality,” Rich writes. “Machine learning methods are exceptional in their capacity at sifting through large quantities of data to find complex correlations among features and accurately predict a classification. In other words, machine learning can be used to analyze vast amounts of information about past criminals and noncriminals and isolate rules that describe when facts about an individual make it likely that he or she is engaged in criminal conduct.”
In the article, Rich examines recent uses of machine learning for crime fighting, then explores both legal and practical challenges to what he calls “automated suspicion algorithms” or ASAs.
A version of Rich’s extended scholarship on this topic, recently identified as a piece of “the best scholarship relevant to the law” and forthcoming in the University of Pennsylvania Law Review, is available on SSRN here.