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The increasing prevalence of Artificial Intelligence (AI) in safety-criticalcontexts such as air-traffic control leads to systems that are practical andefficient, and to some extent explainable to humans to be trusted and accepted.The present structured literature analysis examines n = 236 articles on therequirements for the explainability and acceptance of AI. Results include acomprehensive review of n = 48 articles on information people need to perceivean AI as explainable, the information needed to accept an AI, andrepresentation and interaction methods promoting trust in an AI. Resultsindicate that the two main groups of users are developers who requireinformation about the internal operations of the model and end users whorequire information about AI results or behavior. Users' information needs varyin specificity, complexity, and urgency and must consider context, domainknowledge, and the user's cognitive resources. The acceptance of AI systemsdepends on information about the system's functions and performance, privacyand ethical considerations, as well as goal-supporting information tailored toindividual preferences and information to establish trust in the system.Information about the system's limitations and potential failures can increaseacceptance and trust. Trusted interaction methods are human-like, includingnatural language, speech, text, and visual representations such as graphs,charts, and animations. Our results have significant implications for futurehuman-centric AI systems being developed. Thus, they are suitable as input forfurther application-specific investigations of user needs.

Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work