2017 Wagner Finalist Duke & Harvard

Optimized Scoring Systems: Towards Trust in Machine Learning for Healthcare and Criminal Justice

Questions of trust in machine learning models are becoming increasingly important, as these models are starting to be used widely for high-stakes decisions in medicine and criminal justice. Transparency of models is a key issue affecting trust. The topic of transparency in modeling is being heavily debated in the media, and there are conflicting laws on the use of black box models between the European Union and the United States. This paper reveals that: (1) There is new technology to build transparent machine learning models that are often as accurate as black-box machine learning models. (2) These methods have had impact already in medicine and criminal justice. This work calls into question the overall need for black box models in these applications.