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Towards a Value-driven Explainable Agent for Collective Privacy: Extended Abstract

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationProc. of the 19th International Conference on Autonomous Agents and Multiagent Systems
Accepted/In press4 Mar 2020

Documents

  • AAMAS_Extended_Abstract

    AAMAS_Extended_Abstract.pdf, 365 KB, application/pdf

    Uploaded date:04 Mar 2020

    Version:Accepted author manuscript

King's Authors

Abstract

Online social networks lack support for the collaborative management of access control. This is crucial for content that may involve multiple users such as photos, as this lack of support causes conflicts that lead to privacy violations. Previous research proposed collaborative mechanisms to support users in these cases, but most of these attempts fail to satisfy some desirable requirements, such as explainability, role-agnosticism, adaptability, and being utility- and value-driven at the same time. In this paper, we outline an agent architecture that has been designed to meet all these requirements.

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