On the Potential of Mediation Chatbots for Mitigating Multiparty Privacy Conflicts - A Wizard-of-Oz Study

被引：6
作者：
Salehzadeh Niksirat K. [1 ]
Korka D. [1 ]
Harkous H. [2 ]
Huguenin K. [1 ]
Cherubini M. [1 ]
机构：
[1] University of Lausanne, Lausanne
[2] Google, Zürich
来源：
Proceedings of the ACM on Human-Computer Interaction | 2023年 / 7卷 / CSCW1期
关键词：
chatbot; conversational agents; interdependent privacy; multiparty privacy conflicts; online social networks; privacy;
D O I：
10.1145/3579618
中图分类号：
学科分类号：
摘要：
Sharing multimedia content, without obtaining consent from the people involved causes multiparty privacy conflicts (MPCs). However, social-media platforms do not proactively protect users from the occurrence of MPCs. Hence, users resort to out-of-band, informal communication channels, attempting to mitigate such conflicts. So far, previous works have focused on hard interventions that do not adequately consider the contextual factors (e.g., social norms, cognitive priming) or are employed too late (i.e., the content has already been seen). In this work, we investigate the potential of conversational agents as a medium for negotiating and mitigating MPCs. We designed MediationBot, a mediator chatbot that encourages consent collection, enables users to explain their points of view, and proposes solutions to finding a middle ground. We evaluated our design using a Wizard-of-Oz experiment with N = 32 participants, where we found that MediationBot can effectively help participants to reach an agreement and to prevent MPCs. It produced a structured conversation where participants had well-clarified speaking turns. Overall, our participants found MediationBot to be supportive as it proposes useful middle-ground solutions. Our work informs the future design of mediator agents to support social-media users against MPCs. © 2023 Owner/Author.
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  • [1] Acquisti A., Grossklags J., Privacy Attitudes and Privacy Behavior, Economics of Info. Secu, 12, pp. 165-178, (2004)
  • [2] Acquisti A., Sleeper M., Shomir Wilson Y., Adjerid I., Balebako R., Brandimarte L., Faith Cranor L., Komanduri S., Giovanni Leon P., Sadeh N., Schaub F., 2017 Nudges for Privacy and Security: Understanding and Assisting Users’ Choices Online, ACM Comp. Surv. CSUR, 50, 3, pp. 1-41, (2017)
  • [3] Aditya P., Sen R., Druschel P., Joon Oh S., Benenson R., Fritz M., Schiele B., Bhattacharjee B., Tong Wu T., I-Pic: A Platform for Privacy-Compliant Image Capture, Proc. of the Annual Int. Conf. on Mobile Sys., Applications, and Services (MobiSys). Assoc. for Comp. Mach ACM), Singapore, Singapore, pp. 235-248, (2016)
  • [4] Akkuzu G., Aziz B., Adda M., 2020 Towards consensus-based group decision making for co-owned data sharing in online social networks, IEEE Access, 8, pp. 91311-91325, (2020)
  • [5] Altay S., Hacquin A., Chevallier C., Mercier H., 2021 Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions, Jour. of Experimental Psychology: Applied First Posting, (2021)
  • [6] Altay S., Schwartz M., Hacquin A., Allard A., Blancke S., Mercier H., 2022 Scaling up interactive argumentation by providing counterarguments with a chatbot, Nature Hum. Behaviour, 6, 4, pp. 579-592, (2022)
  • [7] Altman I., The environment and social behavior: Privacy, personal space, territory, crowding, Brooks/Cole Pub. Co., Monterey, California, USA, (1975)
  • [8] Jean Amon M., Hasan R., Hugenberg K., Bertenthal B.I., Kapadia A., Influencing Photo Sharing Decisions on Social Media: A Case of Paradoxical Findings, 2020 IEEE Symp. on Secu. and Privacy (SP). IEEE, San Francisco, California, USA, pp. 79-95, (2020)
  • [9] Andrews P., Manandhar S., De Boni M., Argumentative Human Computer Dialogue for Automated Persuasion, Proc. of the SIGdial Workshop on Discourse and Dialogue (Columbus, Ohio) (SIGdial’08). Assoc. for Computational Linguistics, USA, pp. 138-147, (2008)
  • [10] Miles M.B., Michael Huberman A., Saldana J., Qualitative Data Analysis: A Methods Sourcebook, (2019)
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