Asst. Prof. Ceren BILGICI – Ph.D at Université Paris III-Sorbonne Nouvelle – e-mail: c.bilgici@iku.edu.tr – Res. Asst. Özge ÖZKÖK ŞİŞMAN Istanbul Kultur University- New Media and Communication Department Ph.D. Candidate at Marmara University e-mail: o.ozkok@iku.edu.tr
Depending on the development of artificial intelligence systems, more and more robots have started to take place in the daily life of individuals. To integrate robots into humans’ daily life activities, they must be accepted by users. Robot acceptance is related to the functionality of the robot as well as how people’s interactions with robots are structured (Fronemann, Pollmann, Loh 2022). Today, domestic robots developed in line with the developments in human-robot interaction studies, have started to find a place in modern households and have enabled many users to accept them as a part of their daily lives (Graaf, Allouch and Van Dijk 2016; Graaf, Allouch and Van Dijk 2019; Chatzimichali, Harrison and Chrysostomou 2021). With the increase in types of robots with different functions in human environments, human-robot interactions have also increased significantly. Therefore, individuals have become more and more data providers to these systems. These systems need a lot of data to better understand user needs, and expectations and to improve human-robot interaction accordingly. In this direction, the more user data a system has, the more effective it can be developed for the user (Pagallo 2016). Thus, companies can save and process data about many activities related to the daily lives of users. In this context, the transformation of these artificial intelligence-based devices into a part of individuals’ daily lives has brought along concerns about data surveillance and privacy. For this reason, data governance, surveillance, and privacy have become fundamental concepts prioritized by EU Commission in Ethics Guidelines for trustworthy AI (https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html, Retrieved 02.08.2022) and by Organization for Economic Cooperation and Development (OECD) in Guidelines Governing the Protection of Privacy and Transborder Flows of Personal Data. (https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0188, Retrieved 02.08.2022).
Foucault explained the surveillance society within the framework of the “Panopticon” metaphor emphasized by Bentham, arguing that the consciousness of being watched in social life has power over the behaviors of individuals (Foucault 1995: 356-361). Today, the progress of technological innovations has caused the concept of digital surveillance that emerged in this context has also redrawn the boundaries of data privacy (Lyon, 2013: 11). The rise in interest in robot technologies that require access to all personal data from location services to internet behaviors strengthens the paradox emerging in this framework.
In this framework, this study focuses on human-robot vacuum cleaner interaction. Robot vacuum cleaners that exhibit autonomous behavior, are the first service robots that individuals began to use widely in their homes (Hendriks etc. 2011). From this perspective, the research aims to reveal user perspectives and feelings about their relationships with these robots and to demonstrate their thoughts about data protection, surveillance, and privacy issues in this interaction. In this study, semi-structured in-depth interview method was used to convey the motivations, feelings, and thoughts of the users about their robot vacuum cleaner practices. In-depth interviews were conducted with a total of 12 participants between the ages of 25-56 who are early adopters of robot vacuum cleaners in Turkey. According to the preliminary research findings, it is revealed that the participants anthropomorphize robot vacuum cleaners and this affects their interaction with them in a positive way. Despite data privacy and surveillance concerns, positive thoughts about these devices and their functionality override these concerns.
Keywords: Artificial Intelligence, Robot, Privacy, Data, Surveillance
References
Chatzimichali, A., Harrison, R., & Chrysostomou, D. (2021). Toward privacy-sensitive human-robot interaction: Privacy terms and human–data interaction in the personal robot era. Paladyn, Journal of Behavioral Robotics, 12(1), 160-174.
De Graaf, M. M., Allouch, S. B., & van Dijk, J. A. (2016, March). Long-term acceptance of social robots in domestic environments: insights from a user’s perspective. In 2016 AAAI spring symposium series.
De Graaf, M. M., Ben Allouch, S., & Van Dijk, J. A. (2019). Why would I use this in my home? A model of domestic social robot acceptance. Human-Computer Interaction, 34(2), 115-173.
Foucault, M. (1995), Discipline and Punish: The Birth of the Prison. New York: Vintage Books
Fronemann, N., Pollmann, K., & Loh, W. (2022). Should my robot know what’s best for me? Human-robot interaction between user experience and ethical design. AI & SOCIETY, 37(2), 517-533.
Hendriks, B., Meerbeek, B., Boess, S., Pauws, S., & Sonneveld, M. (2011). Robot vacuum cleaner personality and behavior. International Journal of Social Robotics, 3(2), 187-195
Lyon, D. (2007), Surveillance Studies: An Overview. London: Wiley Press.
Pagallo, U. (2016). The impact of domestic robots on privacy and data protection, and the troubles with legal regulation by design. In Data protection on the move (pp. 387-410). Springer: Dordrecht.
Internet Resources
European Union (2022), from https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html, Retrieved 02.08.2022.
OECD Legal Instruments, (2013), from ttps://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0188 Retrieved 02.08.2022.