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Privacy-Aware Explanations for Team Formation

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PRIMA 2022: Principles and Practice of Multi-Agent Systems (PRIMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13753))

Abstract

Over the recent years there is a growing move towards explainable AI (XAI). The widespread use of AI systems in a large variety of applications that support human’s decisions leads to the imperative need for providing explanations regarding the AI system’s functionality. That is, explanations are necessary for earning the user’s trust regarding the AI systems. At the same time, recent legislation such as GDPR regarding data privacy require that any attempt towards explainability shall not disclose private data and information to third-parties. In this work we focus on providing privacy-aware explanations in the realm of team formation scenarios. We propose the means to analyse whether an explanation leads an explainability algorithm to incur in privacy breaches when computing explanation for a user.

Research supported by projects AI4EU (H2020-825619), TAILOR (H2020-952215), 2019DI17, Humane-AI-Net (H2020-952026), Crowd4SDG (H2020-872944), and grant PID2019-104156GB-I00 funded by MCIN/AEI/10.13039/501100011033.

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Authors and Affiliations

  1. Artificial Intelligence Research Institute, CSIC, Barcelona, Spain

    Athina Georgara, Juan Antonio Rodríguez-Aguilar & Carles Sierra

  2. Enzyme Advising Group, Barcelona, Spain

    Athina Georgara

Authors
  1. Athina Georgara
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  2. Juan Antonio Rodríguez-Aguilar
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  3. Carles Sierra
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Corresponding author

Correspondence to Athina Georgara .

Editor information

Editors and Affiliations

  1. Özyeğin University, Istanbul, Turkey

    Reyhan Aydoğan

  2. Universitat Politècnica de València, Valencia, Spain

    Natalia Criado

  3. Université Paris-Dauphine, Paris, France

    Jérôme Lang

  4. Universitat Politècnica de València, Valencia, Spain

    Victor Sanchez-Anguix

  5. King's College London, London, UK

    Marc Serramia

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Georgara, A., Rodríguez-Aguilar, J.A., Sierra, C. (2023). Privacy-Aware Explanations for Team Formation. In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_32

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  • DOI: https://doi.org/10.1007/978-3-031-21203-1_32

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  • Print ISBN: 978-3-031-21202-4

  • Online ISBN: 978-3-031-21203-1

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