Explainable AI: A Diverse Stakeholder Perspective

Abstract

Artificial Intelligence (AI) is increasingly integral for doing classification and prediction tasks across various fields, including healthcare, legal systems, autonomous vehicles, and financial services [1]. As such, stakeholders such as system developers, system operators, end-users necessitate varying levels of explanations for the decisions proposed by these AI systems to enhance their trust and reliability in these systems, and use these systems in practice. The growing reliance on AI as a decision-support tool in these critical areas underscores the need for AI systems to be explainable development process and architecture, comprehensible to their users, ensuring their use is safe, responsible, and in compliance with legal standards.

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