Effects of Past Interactions on User Experience with Recommended Documents

Jahanbakhsh, Farnaz; Awadallah, Ahmed Hassan; Dumais, Susan T.; Xu, Xuhai

doi:10.1145/3343413.3377977

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Explanations that extend a user's prior knowledge or fulfill their immediate needs should be prioritized [60]. Moreover, previous research has suggested that presenting detailed and personalized explanations is useful for better understanding AI outcomes [78,101,113,192,228].…”

Section: What To Explain?mentioning

confidence: 99%

XAIR: A Framework of Explainable AI in Augmented Reality

Yu²,

Jonker

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

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Section: What To Explain?mentioning

confidence: 99%

XAIR: A Framework of Explainable AI in Augmented Reality

Yu²,

Jonker

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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“…Future work can investigate the types of questions that arise when users interact with other file types such as PDF, Excel, and PowerPoint. Because each file type is used for different purposes (e.g., Excel documents for long-term book-keeping [27]) and possibly containing content at different levels of abstraction, the extent to which question answering in these documents can be automated and the kinds of expertise knowledge workers need may be different from the Word documents in our study.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Understanding Questions that Arise When Working with Business Documents

Jahanbakhsh,

Nouri,

Sim

et al. 2022

Preprint

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While digital assistants are increasingly used to help with various productivity tasks, less attention has been paid to employing them in the domain of business documents. To build an agent that can handle users' information needs in this domain, we must first understand the types of assistance that users desire when working on their documents. In this work, we present results from two user studies that characterize the information needs and queries of authors, reviewers, and readers of business documents. In the first study, we used experience sampling to collect users' questions in-situ as they were working with their documents, and in the second, we built a human-in-the-loop document Q&A system which rendered assistance with a variety of users' questions. Our results have implications for the design of document assistants that complement AI with human intelligence including whether particular skillsets or roles within the document are needed from human respondents, as well as the challenges around such systems.CCS Concepts: • Human-centered computing → Empirical studies in HCI.

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“…The effect of recommendation explanations needs to be evaluated with real users [14]. Existing works usually ask participants to answer surveys after the explanations are displayed.…”

Section: User Reactions Towards Explanationsmentioning

confidence: 99%

Understanding User Behavior For Document Recommendation

Awadallah

Dumais

et al. 2020

Proceedings of the Web Conference 2020

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Personalized document recommendation systems aim to provide users with a quick shortcut to the documents they may want to access next, usually with an explanation about why the document is recommended. Previous work explored various methods for better recommendations and better explanations in different domains. However, there are few efforts that closely study how users react to the recommended items in a document recommendation scenario. We conducted a large-scale log study of users' interaction behavior with the explainable recommendation on one of the largest cloud document platforms office.com. Our analysis reveals a number of factors, including display position, file type, authorship, recency of last access, and most importantly, the recommendation explanations, that are associated with whether users will recognize or open the recommended documents. Moreover, we specifically focus on explanations and conduct an online experiment to investigate the influence of different explanations on user behavior. Our analysis indicates that the recommendations help users access their documents significantly faster, but sometimes users miss a recommendation and resort to other more complicated methods to open the documents. Our results suggest opportunities to improve explanations and more generally the design of systems that provide and explain recommendations for documents. CCS CONCEPTS• Social and professional topics User characteristics; • Humancentered computing Human computer interaction (HCI).

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Effects of Past Interactions on User Experience with Recommended Documents

Cited by 9 publications

References 29 publications

XAIR: A Framework of Explainable AI in Augmented Reality

XAIR: A Framework of Explainable AI in Augmented Reality

Understanding Questions that Arise When Working with Business Documents

Understanding User Behavior For Document Recommendation

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