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Evaluation has always been a key challenge in the development of artificialintelligence (AI) based software, due to the technical complexity of thesoftware artifact and, often, its embedding in complex sociotechnicalprocesses. Recent advances in machine learning (ML) enabled by deep neuralnetworks has exacerbated the challenge of evaluating such software due to theopaque nature of these ML-based artifacts. A key related issue is the(in)ability of such systems to generate useful explanations of their outputs,and we argue that the explanation and evaluation problems are closely linked.The paper models the elements of a ML-based AI system in the context of publicsector decision (PSD) applications involving both artificial and humanintelligence, and maps these elements against issues in both evaluation andexplanation, showing how the two are related. We consider a number of commonPSD application patterns in the light of our model, and identify a set of keyissues connected to explanation and evaluation in each case. Finally, wepropose multiple strategies to promote wider adoption of AI/ML technologies inPSD, where each is distinguished by a focus on different elements of our model,allowing PSD policy makers to adopt an approach that best fits their contextand concerns.

Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation