Under Co-construction: Toward the Social Design of Explainable AI Systems

Abstract

Technological advancements in machine learning affecting humans' lives on the one hand and also regulatory initiatives fostering transparency in algorithmic decision making on the other hand drive a recent surge of interest in explainable AI (XAI). Explainability is discussed as a solution to sociotechnical challenges such as intelligent software providing incomprehensible decisions or big data enabling fast learning but becoming too complex to fully comprehend and judge its achievements. With explainable AI, more insights into the functions, decisions, and usefulness of algorithms are expected.

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