Personalising Explainable Recommendations: Literature and Conceptualisation

Naiseh, Mohammad; Jiang, Nan; Ma, Jianbing; Ali, Raian

doi:10.1007/978-3-030-45691-7_49

Cited by 21 publications

(13 citation statements)

References 40 publications

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“…For instance, simple feedback such as "explain more", "redundant explanation" or "different explanation" can support users who wish to involve with the explanations and improve the explanations in future interactions. In a previous paper [61], we reported on the results related to input modalities meant for tailoring the explanations for a specific user or group of users i.e. personalisation.…”

Section: Discussion and Research Challengesmentioning

confidence: 99%

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Naiseh

Jiang

et al. 2020

Research Challenges in Information Science

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With the increase in data volume, velocity and types, intelligent human-agent systems have become popular and adopted in different application domains, including critical and sensitive areas such as health and security. Humans' trust, their consent and receptiveness to recommendations are the main requirement for the success of such services. Recently, the demand on explaining the recommendations to humans has increased both from humans interacting with these systems so that they make an informed decision and, also, owners and systems managers to increase transparency and consequently trust and users' retention. Existing systematic reviews in the area of explainable recommendations focused on the goal of providing explanations, their presentation and informational content. In this paper, we review the literature with a focus on two user experience facets of explanations; delivery methods and modalities. We then focus on the risks of explanation both on user experience and their decision making. Our review revealed that explanations delivery to end-users is mostly designed to be along with the recommendation in a push and pull styles while archiving explanations for later accountability and traceability is still limited. We also found that the emphasis was mainly on the benefits of recommendations while risks and potential concerns, such as over-reliance on machines, is still a new area to explore.

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Section: Discussion and Research Challengesmentioning

confidence: 99%

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Naiseh

Jiang

et al. 2020

Research Challenges in Information Science

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“…Sokol et al [19] present 11 usability requirements for the explanations which are: Soundness, Completeness, Contextfullness, Interactiveness, Actionability, Chronology, Coherence, Novelty, Complexity and Personalisation. Also, the initial findings from this PhD research provide in-depth investigation about the conceptualisation of the personalisation aspect in a previous work [14]. Since the implementation of these aspects has been limited to low stake applications, these principles and findings may not translate to high stake applications where trust calibration and safety are crucial requirements.…”

Section: Human-computer Interaction (Hci) and Explainabilitymentioning

confidence: 93%

Explainability Design Patterns in Clinical Decision Support Systems

Naiseh

2020

Research Challenges in Information Science

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This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are explainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.

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“…This means, explanation alone is not enough but rather its delivery and presentation should adapt to the user and task context to avoid that conflict. Also, our previous literature review revealed that explanation shall be adapted to users' level of knowledge and expertise in a given Human-AI collaborative decision-making task [56]. Similarly, previous studies showed that adaptive information increases collaborative Human-Robot performance [82].…”

Section: Adaptationmentioning

confidence: 94%

Explainable recommendation: when design meets trust calibration

et al. 2021

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Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive connotations, studies showed that the assumption behind users interacting and engaging with these explanations could introduce trust calibration errors such as facilitating irrational or less thoughtful agreement or disagreement with the AI recommendation. In this paper, we explore how to help trust calibration through explanation interaction design. Our research method included two main phases. We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools. Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. As a conclusion of our research, we provide five design principles: Design for engagement, challenging habitual actions, attention guidance, friction and support training and learning. Our findings are meant to pave the way towards a more integrated framework for designing explanations with trust calibration as a primary goal.

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Personalising Explainable Recommendations: Literature and Conceptualisation

Cited by 21 publications

References 40 publications

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Explainability Design Patterns in Clinical Decision Support Systems

Explainable recommendation: when design meets trust calibration

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