Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence
It addresses the problem of fragmented discussions among stakeholders like computer scientists and ethicists regarding explainable AI, but is incremental as it builds on existing philosophical literature.
The paper tackles the lack of a common framework to bridge technical, epistemic, and normative aspects in debates about opaque AI systems for consequential decisions, by proposing a philosophical framework that identifies relevant explanation types, incorporates social and ethical values, and improves algorithmic design.
The societal and ethical implications of the use of opaque artificial intelligence systems for consequential decisions, such as welfare allocation and criminal justice, have generated a lively debate among multiple stakeholder groups, including computer scientists, ethicists, social scientists, policy makers, and end users. However, the lack of a common language or a multi-dimensional framework to appropriately bridge the technical, epistemic, and normative aspects of this debate prevents the discussion from being as productive as it could be. Drawing on the philosophical literature on the nature and value of explanations, this paper offers a multi-faceted framework that brings more conceptual precision to the present debate by (1) identifying the types of explanations that are most pertinent to artificial intelligence predictions, (2) recognizing the relevance and importance of social and ethical values for the evaluation of these explanations, and (3) demonstrating the importance of these explanations for incorporating a diversified approach to improving the design of truthful algorithmic ecosystems. The proposed philosophical framework thus lays the groundwork for establishing a pertinent connection between the technical and ethical aspects of artificial intelligence systems.