The AI-DEC: A Card-based Design Method for User-centered AI Explanations

Explainable AI (XAI)AI-Assisted Decision-Making & AutomationPrivacy by Design & User ControlPhysicians, Nurses & CliniciansAI/ML Researchers & EngineersPersonal Finance UsersHCI Researchers

Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called \textit{AI-DEC}, that defines four dimensions of AI explanations that are critical to the integration of AI systems in the workplace---communication content, modality, frequency, and direction---and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system's workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.

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https://hci.top/en/papers/dis/164655/2024

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DIS
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2024
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3 authors
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Explainable AI (XAI), AI-Assisted Decision-Making & Automation, Privacy by Design & User Control
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Physicians, Nurses & Clinicians, AI/ML Researchers & Engineers, Personal Finance Users, HCI Researchers
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Abstract only
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