ANALYZING THE COMPAS ALGORITHM IN CRIMINAL DEFENDANT RISK ASSESSMENT

Abstract

For my thesis, I analyzed the COMPAS recidivism prediction tool made by Equivant which aims to see how likely a defendant charged with a crime will re-offending given a score from 1-10 where 1 indicating lowest risk and 10 indicating highest risk and is used by many states in the country. ProPublica dataset consisted of re-arrest data of COMPAS predictions of 6172 people made between 2012-2014 which they proved that COMPAS was more likely to falsely label African-American defendants as high risk more often than White defendants and more likely to falsely label White defendants as low risk more often than African-American defendants. Looking at ProPublcia’s dataset along with Jai Nimgaonkar’s dataset who took ProPublica’s dataset to see if these people were convicted of a crime in order to see if there is still bias from re-arrest data to conviction data when looking at intersectionality between sex and race and different fairness aware algorithms.