Shared Mental Model Processing in Visualization Technologies: A Review of Fundamental Concepts and a Guide to Future Research in Human-Computer Interaction

Yusoff, Nor'ain Mohd; Salim, Siti Salwah

doi:10.1007/978-3-030-49044-7_20

Cited by 5 publications

(1 citation statement)

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“…There is an interest in leveraging cognitive models, such as MMs, to better understand how humans perceive, reason, and make decisions. By incorporating these models into the RE process, researchers aim to develop explanations that align with users' cognitive processes, enhancing the effectiveness and usability of AI systems [6], [13], [27], [28]. These models offer promising prospects of capturing and representing the cognitive processes involved in explainability needs in human end-users.…”

Section: Methods Referencesmentioning

confidence: 99%

A Conceptual Model Framework for XAI Requirement Elicitation of Application Domain System

Aslam,

Segura-Velandia,

Goh

2023

IEEE Access

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The use of data analytics and Machine Learning (ML) branches of AI for predictive and analytic knowledge retrieval has surged significantly in various industries (e.g., health, finance, business, and manufacturing). However, the acceptance of AI has been hindered by opaque models that lack transparency. Explainability in AI (XAI) has gained significant prominence owing to its focus on introducing avenues of accountability in AI. XAI acknowledges the importance of human factors and strives to incorporate them into the design process, recognising that the cognitive effort involved in understanding explanations is a key aspect. Mental Models play a crucial role in the XAI evaluative premise, but their current utility is limited. By intentionally designing explanations that align with users' mental models, their experiences can be significantly enhanced, leading to improved understanding, satisfaction, trust, and performance. This study proposes using Mental Models to elicit explainability requirements and to develop an Ontology-Driven Conceptual Model to facilitate the learning process for a better understanding of explanations.

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