Extending System Performance Past the Boundaries of Technical Maturity: Human-Agent Teamwork Perspective for Industrial Inspection

Cabour, Garrick; Ledoux, Emmanuel; Bassetto, Samuel

doi:10.48550/arxiv.2102.03241

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“…User Activity (the "what and "how"): focus on the situated aspect of SMEs' work encompassing problem-solving strategies, meaning-making, and decision-making processes, knowledge applied, and skills required [5] that allow them to operate successfully in the domain [54]. The goal is to deepen the analysis of expertise to uncover how operators deal with the domain's characteristics (such as work requirements and institution rules), the inherent variability of processes, the decentralization of work, and the dynamic nature of contemporary work environments [54,61,62].…”

Section: Inferring Explanation Needs Through User Studiesmentioning

confidence: 99%

Towards an Explanation Space to Align Humans and Explainable-AI Teamwork

Cabour,

Morales,

Ledoux

et al. 2021

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Providing meaningful and actionable explanations to end-users is a fundamental prerequisite for implementing explainable intelligent systems in the real world. Explainability is a situated interaction between a user and the AI system rather than being static design principles. The content of explanations is context-dependent and must be defined by evidence about the user and its context. This paper seeks to operationalize this concept by proposing a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users mental models, (2) the end-users cognitive process, (3) the user interface, (4) the human-explainer agent, and the (5) agent process. We first define each component of the architecture. Then we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture's components to support designers in systematically aligning explanations with the end-users work practices, needs, and goals. It guides the specifications of what needs to be explained (content -end-users mental model), why this explanation is necessary (context -end-users cognitive process), to delimit how to explain it (format -human-explainer agent and user interface), and when should the explanations be given. We then exemplify the tool's use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations/areas for improvement, and future work to be done.

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Section: Inferring Explanation Needs Through User Studiesmentioning

confidence: 99%