The Tensions Between Explainable AI and Good Public Policy

The author considers the complexity of using algorithmic decision-making in policy-sensitive areas, like determining criminal bail and sentences and welfare benefits claims and argues that advances in explainability techniques are necessary, but not sufficient, for resolving key questions about such decisions. She argues that the inherent complexity of the most powerful AI models and our inability to reduce law and regulation to clearly stated optimization goals for the algorithm reinforce the need for transparent governance by model users, especially when they are government agencies..