Scoring systems: At the extreme of interpretable machine learning – Cynthia Rudin – Duke University

With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Interpretability of machine learning models is critical in high stakes decisions.

In this talk, Professor Rudin will focus on one of the most fundamental and important problems in the field of interpretable machine learning: optimal scoring systems. Scoring systems are sparse linear models with integer coefficients. Such models first started to be used ~100 years ago. Generally, such models are created without data, or are constructed by manual feature selection and rounding logistic regression coefficients, but these manual techniques sacrifice performance; humans are not naturally adept at high-dimensional optimization.

Professor Rudin will present the first practical algorithm for building optimal scoring systems from data. This method has been used for several important applications to healthcare and criminal justice.

This discussion will mainly focus on work from three papers:

Learning Optimized Risk Scores. Journal of Machine Learning Research, 2019

The Age of Secrecy and Unfairness in Recidivism Prediction. Harvard Data Science Review, 2020

Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. JAMA Neurology, 2017

Introduction

This conference – organized under the auspices of the Isaac Newton Institute “Mathematics of Deep Learning” Programme — brings together leading researchers along with other stakeholders in industry and society to discuss issues surrounding trustworthy artificial intelligence.

This conference will overview the state-of-the-art within the wide area of trustworthy artificial intelligence including machine learning accountability, fairness, privacy, and safety; it will overview emerging directions in trustworthy artificial intelligence, and engage with academia, industry, policy makers, and the wider public.