Envisioning a collaborative smart home solution based on argumentative dialogues

Mocanu, Andrei

doi:10.1145/2801081.2801098

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…The authors of one study [166] argued that this aspect is particularly critical because "[the] human user bears the ultimate responsibility for action" and, therefore, she should be able to explain the decision. Transparency is also seen as key for users to develop trust toward the system [68,244,154]. In some studies that focus on transparency, trust is thus not the direct purpose of the system's explanation facility, but an expected indirect effect of transparency.…”

Section: Purpose Of Explanationsmentioning

confidence: 99%

“…Transparency is also seen as key for users to develop trust toward the system [68,244,154]. In some studies that focus on transparency, trust is thus not the direct purpose of the system's explanation facility, but an expected indirect effect of transparency.…”

Section: Purpose Of Explanationsmentioning

confidence: 99%

“…In reality, as stated by Tintarev and Masthoff [222] and others, the possible purposes can, however, be related in different ways. Achieving transparency can, for example, constitute one of several factors that contribute to user trust in the system [68,244,154,165]. Moreover, trust is usually not even the ultimate goal from the perspective of the provider of the advice-giving system, who might be interested in increasing the user intention to continue to use the system in the future.…”

Section: General Facets Of Explanationsmentioning

confidence: 99%

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A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

295

193

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With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

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