Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice

Abstract. Questions of trust in machine-learning models are becoming increasingly important as these tools are starting to be used widely for high-stakes decisions in medicine and criminal justice. Transparency of models is a key aspect affecting trust. This paper reveals that there is new technology to build transparent machine-learning models that are often as accurate as black-box machine-learning models. These methods have already had an impact in medicine and criminal justice. This work calls into question the overall need for black-box models in these applications. Copyright:

Duke Scholars

Author Cynthia D. Rudin Computer Science