A Model for Governing Information Sharing in Smart Assistants

Zhan, Xiao; Sarkadi, Ştefan; Criado, Natalia; Such, José M.

doi:10.1145/3514094.3534129

Cited by 10 publications

(15 citation statements)

References 43 publications

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“…Here, the receiver of the photo is the context that determines the preferences of the user. Therefore, while Zhan et al's work [38] seems promising, we argue that a more general approach is needed. Note that constructing predictions based on context requires knowledge of the possible contexts and their connections, and this knowledge is dependent on the domain.…”

Section: Related Workmentioning

confidence: 77%

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Collaborative Filtering to Capture AI User’s Preferences as Norms

Serramia

Criado²,

Lück³

2022

PRIMA 2022: Principles and Practice of Multi-Agent Systems

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Customising AI technologies to each user's preferences is fundamental to them functioning well. Unfortunately, current methods require too much user involvement and fail to capture their true preferences. In fact, to avoid the nuisance of manually setting preferences, users usually accept the default settings even if these do not conform to their true preferences. Norms can be useful to regulate behaviour and ensure it adheres to user preferences but, while the literature has thoroughly studied norms, most proposals take a formal perspective. Indeed, while there has been some research on constructing norms to capture a user's privacy preferences, these methods rely on domain knowledge which, in the case of AI technologies, is difficult to obtain and maintain. We argue that a new perspective is required when constructing norms, which is to exploit the large amount of preference information readily available from whole systems of users. Inspired by recommender systems, we believe that collaborative filtering can offer a suitable approach to identifying a user's norm preferences without excessive user involvement.

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Section: Related Workmentioning

confidence: 77%

“…While the ap-proach in this paper would be useful for specifying default preferences, it is not able to tailor them to each individual user. Using that dataset, Zhan et al [38] presented a model to predict privacy norms. Here, norms are predicted using a machine learning model with an accuracy of 70-80%.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering to Capture AI User’s Preferences as Norms

Serramia

Criado²,

Lück³

2022

PRIMA 2022: Principles and Practice of Multi-Agent Systems

Self Cite

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“…Several scholars argue about customer scepticism and acceptance of conversational commerce in online shopping (Devi and Chanda, 2022) as well as ethical concerns with voice assistants and the privacy risk related to the disclosure of sensitive user data (Seymour et al. , 2022; Zhan et al. , 2022).…”

Section: Theoretical Background and Research Objectivesmentioning

confidence: 99%

How shopping habits change with artificial intelligence: smart speakers' usage intention

Aiolfi

2023

IJRDM

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PurposeThe research aims to understand how smart speakers are perceived by their actual and potential users, their attitude towards smart speakers and consequently their intention to use them.Design/methodology/approachThe authors apply a structural equation modelling (SEM) approach to test the research hypotheses through data coming from a structured questionnaire.FindingsThe results show that the higher the importance attributed to usefulness and ease of use, the higher the positive attitude that in turn positively affects the intention to use smart speakers. A significant relationship also emerged between task technology fit and attitude towards smart speakers, as well as between perceived enjoyment and attitude towards smart speakers. Perceived privacy risk, innovativeness and social attraction have been found to not significantly impact attitudes towards smart speakers.Originality/valueAlthough several academic studies have focused on various aspects of smart technologies, only a few studies discuss the factors that push consumers to use smart speakers for activities related to commercial transactions. Therefore, looking at the rapid rise of smart speakers for daily tasks and the gradual acceptance of voice interaction with digital tools, the authors proposed a study about Italian users' intention to use smart speakers. Specifically, to fill the gap in the existing literature, the authors applied a SEM approach to identify utilitarian and hedonic benefits that motivate the use of these devices.

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“…Norms could be mined in advance from a representative set of people based on contextual integrity [35] and used as defaults. Users could also be given the opportunity at first use (or later) to choose from clusters of norms that usually occur together, have their norms inferred by answering indicative questions about example scenarios, or have them automatically refined over time [30,54]. These norms would then be applied to requests for data sharing by skills with users notified as appropriate (perhaps through a notice similar to those above).…”

Section: Reducing the Number Of Consent Decisionsmentioning

confidence: 99%

Legal Obligation and Ethical Best Practice: Towards Meaningful Verbal Consent for Voice Assistants

Seymour,

Cote,

Such

2023

Preprint

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To improve user experience, Alexa now allows users to consent to data sharing via voice rather than directing them to the companion smartphone app. While verbal consent mechanisms for voice assistants (VAs) can increase usability, they can also undermine principles core to informed consent. We conducted a Delphi study with experts from academia, industry, and the public sector on requirements for verbal consent in VAs. Candidate requirements were drawn from the literature, regulations, and research ethics guidelines that participants rated based on their relevance to the consent process, actionability by platforms, and usability by end-users, discussing their reasoning as the study progressed. We highlight key areas of (dis)agreement between experts, deriving recommendations for regulators, skill developers, and VA platforms towards crafting meaningful verbal consent mechanisms. Key themes include approaching permissions according to the user's ability to opt-out, minimising consent decisions, and ensuring platforms follow established consent principles. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; Usability in security and privacy; • Human-centered computing → Natural language interfaces; • Applied computing → Law.

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A Model for Governing Information Sharing in Smart Assistants

Cited by 10 publications

References 43 publications

Collaborative Filtering to Capture AI User’s Preferences as Norms

Collaborative Filtering to Capture AI User’s Preferences as Norms

How shopping habits change with artificial intelligence: smart speakers' usage intention

Legal Obligation and Ethical Best Practice: Towards Meaningful Verbal Consent for Voice Assistants

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