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arXiv · 2407.10662

XEQ Scale for Evaluating XAI Experience Quality

Anjana Wijekoon·Nirmalie Wiratunga·David Corsar·Kyle Martin·Ikechukwu Nkisi-Orji·Belen Díaz-Agudo·Derek Bridge
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Abstract

Explainable Artificial Intelligence (XAI) aims to improve the transparency of autonomous decision-making through explanations. Recent literature has emphasised users' need for holistic "multi-shot" explanations and personalised engagement with XAI systems. We refer to this user-centred interaction as an XAI Experience. Despite advances in creating XAI experiences, evaluating them in a user-centred manner has remained challenging. In response, we developed the XAI Experience Quality (XEQ) Scale. XEQ quantifies the quality of experiences across four dimensions: learning, utility, fulfilment and engagement. These contributions extend the state-of-the-art of XAI evaluation, moving beyond the one-dimensional metrics frequently developed to assess single-shot explanations. This paper presents the XEQ scale development and validation process, including content validation with XAI experts, and discriminant and construct validation through a large-scale pilot study. Our pilot study results offer strong evidence that establishes the XEQ Scale as a comprehensive framework for evaluating user-centred XAI experiences.

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Anjana Wijekoon, Nirmalie Wiratunga, David Corsar, Kyle Martin, Ikechukwu Nkisi-Orji, Belen Díaz-Agudo, Derek Bridge. 2024-07-15. XEQ Scale for Evaluating XAI Experience Quality. https://arxiv.org/abs/2407.10662

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