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A Model for Governing Information Sharing in Smart Assistants

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationAIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
PublisherAssociation for Computing Machinery, Inc
Pages845-855
Number of pages11
ISBN (Electronic)9781450392471
DOIs
Accepted/In press17 Jul 2022
Published26 Jul 2022
Event5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2022 - Oxford, United Kingdom
Duration: 1 Aug 20223 Aug 2022

Publication series

NameAIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Conference

Conference5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2022
Country/TerritoryUnited Kingdom
CityOxford
Period1/08/20223/08/2022

Bibliographical note

Funding Information: We would like to thank the anonymous AIES reviewers for their helpful feedback. This research was partially supported by UKRI through REPHRAIN (EP/V011189/1), the UK’s Research centre on Privacy, Harm Reduction and Adversarial Influence online, as part of its PRAISE inaugural project, and Xiao Zhan is funded by King’s PGR International Scholarship. Publisher Copyright: © 2022 ACM.

Documents

  • zhan2022model

    zhan2022model.pdf, 854 KB, application/pdf

    Uploaded date:14 Jun 2022

    Version:Accepted author manuscript

    Licence:CC BY

King's Authors

Abstract


Smart Personal Assistants (SPAs), such as Amazon Alexa, Google Assistant and Apple Siri, leverage different AI techniques to provide convenient help and assistance to users. However, inappropriate information sharing decisions can lead SPAs to incorrectly disclose user information to undesired parties, or mistakenly block their reasonable access in specific scenarios to desired parties. In fact, reports about privacy violations in SPAs and associated user con- cerns are well known and understood in the related literature. It is difficult for SPAs to automatically decide how data should be shared with respect to the privacy preferences of the users. We argue norms, which are regarded as shared standards of acceptable behaviour of groups and/or individuals, can be used to govern and reason about the best course of action of SPAs with regards to information sharing, and our work is the first to propose a practi- cal model to address the above issues and govern SPAs based on normative systems and the contextual integrity theory of privacy. We evaluated the performance of the model using a real dataset of user preferences for privacy in SPAs and the results showed a very marked and significant improvement in understanding user preferences and making the right decisions with respect to data sharing.

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