Influence of context on users’ views about explanations for decision-tree predictions

Maruf, Sameen; Zukerman, Ingrid; Reiter, Ehud; Haffari, Gholamreza

doi:10.1016/j.csl.2023.101483

Cited by 2 publications

References 51 publications

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Evaluation of Human-Understandability of Global Model Explanations Using Decision Tree

Sivaprasad,

Reiter,

Tintarev

et al. 2024

Communications in Computer and Information Science

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In explainable artificial intelligence (XAI) research, the predominant focus has been on interpreting models for experts and practitioners. Model agnostic and local explanation approaches are deemed interpretable and sufficient in many applications. However, in domains like healthcare, where end users are patients without AI or domain expertise, there is an urgent need for model explanations that are more comprehensible and instil trust in the model’s operations. We hypothesise that generating model explanations that are narrative, patient-specific and global (holistic of the model) would enable better understandability and enable decision-making. We test this using a decision tree model to generate both local and global explanations for patients identified as having a high risk of coronary heart disease. These explanations are presented to non-expert users. We find a strong individual preference for a specific type of explanation. The majority of participants prefer global explanations, while a smaller group prefers local explanations. A task based evaluation of mental models of these participants provide valuable feedback to enhance narrative global explanations. This, in turn, guides the design of health informatics systems that are both trustworthy and actionable.

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