Machine Learning for Policymakers

Résumé du livre

Machine learning matters. In fields as diverse as healthcare, transportation, policing, and warfighting, machine learning algorithms have already had a significant impact. They seem poised to do more, and the particulars and the implications of this change deserve attention. But machine learning can seem incomprehensible. This paper aims to enable that understanding. First, we introduce and differentiate three types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning. With key concepts identified, we next examine how machine learning is poised to be of still greater significance in areas of importance to policymakers. Fourth, we outline some general matters that deserve attention when it comes to machine learning, such as bias, privacy, explainability, and security. Fifth, we make recommendations to chart a path forward. It is essential that the bias in already-deployed machine learning algorithms be understood, and that ethics and impacts of machine learning are considered going forward. Few areas of national policymaking will remain untouched by artificial intelligence. Though the challenges it poses are complex, the opportunities it o ers are tremendous. Simply put, machine learning is too important to ignore.