  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 AI-DEC, that defines four dimensions of AI explanations that are
critical for the integration of AI systems -- 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.
