ABSTRACT
The area of unfair treatment by artificial intelligences in human-AI interaction has seen frequent attention over the recent years. However, research in this area tends to target one-on-one interaction. Experiments which focus on perceived unfairness in group settings that involve an AI are mostly nonexistent. This work intends to provide insight into settings such as these through conducting a comparative study that exposes groups of people to AIs which treat parts of the participants differently than others in a cooking setting. Our results show significant differences between participants who have been treated unfairly by the AI, but also in groups not directly affected by the unfair treatment; the latter also thought worse of the AI if they felt another group partner was treated unfairly. We discuss these results and theorize about possible reasons
- Abdul, A., Vermeulen, J., Wang, D., Lim, B.Y. and Kankanhalli, M. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada, Apr. 2018), 1–18. DOI:https://doi.org/10.1145/3173574.3174156.Google ScholarDigital Library
- Alexander, S. and Ruderman, M. 1987. The role of procedural and distributive justice in organizational behavior. Social justice research. 1, 2 (1987), 177–198. DOI:https://doi.org/10.1007/BF01048015.Google Scholar
- Bae Brandtzæg, P.B., Skjuve, M., Kristoffer Dysthe, K.K. and Følstad, A. 2021. When the Social Becomes Non-Human: Young People's Perception of Social Support in Chatbots. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, May 2021), 1–13. DOI:https://doi.org/10.1145/3411764.3445318 .Google ScholarDigital Library
- Bartneck, C., Kulic, D., Croft, E. and Zoghbi, S. 2008. Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics. 1, (Jan. 2008), 71–81. DOI:https://doi.org/10.1007/s12369-008-0001-3.Google Scholar
- Bicchieri, C. 1999. Local Fairness. Philosophy and Phenomenological Research. 59, 1 (Feb. 1999), 229–236.Google ScholarCross Ref
- Brandtzaeg, P.B. and Følstad, A. 2018. Chatbots: changing user needs and motivations. Interactions. 25, 5 (Aug. 2018), 38–43. DOI:https://doi.org/10.1145/3236669.Google ScholarDigital Library
- Brandtzaeg, P.B. and Følstad, A. 2017. Why People Use Chatbots. Internet Science (Cham, 2017), 377–392.DOI:https://doi.org/10.1007/978-3-319-70284-1_30 .Google ScholarDigital Library
- Colquitt, J.A., Zapata-Phelan, C.P. and Roberson, Q.M. 2005. Justice in Teams: A Review of Fairness Effects in Collective Contexts. Research in Personnel and Human Resources Management. J. J. Martocchio, ed. Emerald Group Publishing Limited. 53–94.DOI: https://doi.org/10.1016/S0742-7301(05)24002-1.Google Scholar
- Curhan, J.R., Elfenbein, H.A. and Xu, H. 2006. What do people value when they negotiate? Mapping the domain of subjective value in negotiation. Journal of Personality and Social Psychology. 91, 3 (2006), 493–512. DOI:https://doi.org/10.1037/0022-3514.91.3.493.Google ScholarCross Ref
- Doherty, M. 1999. Sprachspezifische Aspekte der Informationsverteilung. Walter de Gruyter GmbH & Co KG.Google Scholar
- Følstad, A., Araujo, T., Law, E.L.-C., Brandtzaeg, P.B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P. and Ischen, C. 2021. Future directions for chatbot research: an interdisciplinary research agenda. Computing. 103, 12 (2021), 2915–2942. DOI:https://doi.org/10.1007/s00607-021-01016-7.Google ScholarDigital Library
- Følstad, A. and Taylor, C. 2020. Conversational Repair in Chatbots for Customer Service: The Effect of Expressing Uncertainty and Suggesting Alternatives. Chatbot Research and Design (Cham, 2020), 201–214. DOI:https://doi.org/10.1007/978-3-030-39540-7_14.Google ScholarDigital Library
- Ghandeharioun, A., McDuff, D., Czerwinski, M. and Rowan, K. 2019. EMMA: An Emotion-Aware Wellbeing Chatbot. 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (Sep. 2019), 1–7. DOI:https://doi.org/10.1109/ACII.2019.8925455.Google Scholar
- Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M. and Wallach, H. 2019. Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2019 CHI conference on human factors in computing systems (2019), 1–16. DOI:https://doi.org/10.1145/3290605.3300830.Google ScholarDigital Library
- Jung, M.F., Difranzo, D., Shen, S., Stoll, B., Claure, H. and Lawrence, A. 2020. Robot-Assisted Tower Construction—A Method to Study the Impact of a Robot's Allocation Behavior on Interpersonal Dynamics and Collaboration in Groups. ACM Transactions on Human-Robot Interaction. 10, 1 (Oct. 2020), 1–23. DOI:https://doi.org/10.1145/3394287.Google ScholarDigital Library
- Keyes, O. 2018. The misgendering machines: Trans/HCI implications of automatic gender recognition. Proceedings of the ACM on human-computer interaction. 2, CSCW (2018), 1–22. DOI:https://doi.org/10.1145/3274357.Google ScholarDigital Library
- Kim, S., Eun, J., Oh, C., Suh, B. and Lee, J. 2020. Bot in the bunch: Facilitating group chat discussion by improving efficiency and participation with a chatbot. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–13. DOI:https://doi.org/10.1145/3313831.3376785.Google ScholarDigital Library
- Klatte, B. and Sackmann, S.A. 2014. Kommunikative Wissensverteilung in Gruppen: Bestimmungsmerkmale, Ansprüche und Implikationen. disserta Verlag.Google Scholar
- Lee, M.K. 2018. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society. 5, 1 (2018), DOI:https://doi.org/10.1177/2053951718756684 .Google Scholar
- Lee, M.K. and Baykal, S. 2017. Algorithmic mediation in group decisions: Fairness perceptions of algorithmically mediated vs. discussion-based social division. Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing (2017), 1035–1048. DOI:https://doi.org/10.1145/2998181.2998230 .Google ScholarDigital Library
- Lee, N., Madotto, A. and Fung, P. 2019. Exploring Social Bias in Chatbots using Stereotype Knowledge. WNLP@ ACL (2019), 177–180.Google Scholar
- Lehr, A., Kammerer, M., Konerding, K.-P., Storrer, A., Thimm, C. and Wolski, W. eds. 2011. Sprache im Alltag: Beiträge zu neuen Perspektiven in der Linguistik. Herbert Ernst Wiegand zum 65. Geburtstag gewidmet. De Gruyter.Google Scholar
- Lin, Z., Xu, P., Winata, G.I., Siddique, F.B., Liu, Z., Shin, J. and Fung, P. 2020. CAiRE: An Empathetic Neural Chatbot. arXiv:1907.12108 [cs]. (Apr. 2020).DOI:https://doi.org/10.48550/arXiv.1907.12108.Google Scholar
- Lutz, B. 2013. Wissen im Dialog: Beiträge zu den Kremser Wissensmanagement-Tagen 2012. Edition Donau-Universität Krems.Google Scholar
- Ma, P., Wang, S. and Liu, J. 2020. Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models. IJCAI (2020), 458–465.Google Scholar
- McFarlin, D.B. and Sweeney, P.D. 1992. Distributive and procedural justice as predictors of satisfaction with personal and organizational outcomes. Academy of management Journal. 35, 3 (1992), 626–637. DOI:https://doi.org/10.5465/256489.Google Scholar
- Morrissey, K. and Kirakowski, J. 2013. ‘Realness’ in Chatbots: Establishing Quantifiable Criteria. Human-Computer Interaction. Interaction Modalities and Techniques. Springer Berlin Heidelberg. 87–96. DOI:https://doi.org/10.1007/978-3-642-39330-3_10 .Google ScholarDigital Library
- Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H. and Hagita, N. 2009. Footing in human-robot conversations: How robots might shape participant roles using gaze cues. (Jan. 2009), 61–68. DOI:https://doi.org/10.1145/1514095.1514109.Google ScholarDigital Library
- Onnasch, L. and Roesler, E. 2020. A Taxonomy to Structure and Analyze Human–Robot Interaction. International Journal of Social Robotics. (Jun. 2020). DOI:https://doi.org/10.1007/s12369-020-00666-5.Google Scholar
- Radhakrishnan, J. and Gupta, S. 2020. Artificial Intelligence in Practice – Real-World Examples and Emerging Business Models. Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation (Cham, 2020), 77–88. DOI:https://doi.org/10.1007/978-3-030-64849-7_8.Google ScholarCross Ref
- Reeves, B. and Nass, C.I. 1996. The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.Google Scholar
- Riedl, M.O. 2019. Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies. 1, 1 (2019), 33–36. DOI:https://doi.org/10.1002/hbe2.117.Google ScholarCross Ref
- Rosner, B., Glynn, R.J. and Lee, M.-L.T. 2006. The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data. Biometrics. 62, 1 (2006), 185–192. DOI:https://doi.org/10.1111/j.1541-0420.2005.00389.x.Google ScholarCross Ref
- Sawilowsky, S.S. 1990. Nonparametric Tests of Interaction in Experimental Design. Review of Educational Research. 60, 1 (Mar. 1990), 91–126. DOI:https://doi.org/10.3102/00346543060001091.Google ScholarCross Ref
- Sebo, S., Stoll, B., Scassellati, B. and Jung, M.F. 2020. Robots in groups and teams: a literature review. Proceedings of the ACM on Human-Computer Interaction. 4, CSCW2 (2020), 1–36. DOI:https://doi.org/10.1145/3415247.Google ScholarDigital Library
- Shen, S., Slovak, P. and Jung, M.F. 2018. Stop. I See a Conflict Happening.: A Robot Mediator for Young Children's Interpersonal Conflict Resolution. Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (New York, NY, USA, Feb. 2018), 69–77. DOI:https://doi.org/10.1145/3171221.3171248.Google ScholarDigital Library
- Skjuve, M. and Brandzaeg, P.B. 2019. Measuring User Experience in Chatbots: An Approach to Interpersonal Communication Competence. Internet Science (Cham, 2019), 113–120. DOI:https://doi.org/10.1007/978-3-030-17705-8_10.Google Scholar
- Srivastava, B., Rossi, F., Usmani, S. and Bernagozzi, M. 2020. Personalized chatbot trustworthiness ratings. IEEE Transactions on Technology and Society. 1, 4 (2020), 184–192. DOI:https://doi.org/10.1109/TTS.2020.3023919.Google ScholarCross Ref
- Vázquez, M., Carter, E.J., McDorman, B., Forlizzi, J., Steinfeld, A. and Hudson, S.E. 2017. Towards Robot Autonomy in Group Conversations: Understanding the Effects of Body Orientation and Gaze. Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (New York, NY, USA, Mar. 2017), 42–52.Google ScholarDigital Library
- Zheng, Q., Tang, Y., Liu, Y., Liu, W. and Huang, Y. 2022. UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library. CHI Conference on Human Factors in Computing Systems (New York, NY, USA, Apr. 2022), 1–24. DOI:https://doi.org/10.1145/3491102.3501855.Google ScholarDigital Library
Index Terms
- The Influence of Unequal Chatbot Treatment on Users in Group Chat
Recommendations
Chatbot or Chat-Blocker: Predicting Chatbot Popularity before Deployment
DIS '21: Proceedings of the 2021 ACM Designing Interactive Systems ConferenceChatbots are widely employed in various scenarios. However, given the high costs of chatbot development and chatbots’ tremendous social influence, chatbot failures may inevitably lead to a huge economic loss. Previous chatbot evaluation frameworks rely ...
CHAT for chat
This mixed methods study addresses learning in online chat virtual reference service at a large university library. Cultural-Historical Activity Theory (CHAT) provides the guiding principle and philosophy for the investigation, understanding, and ...
Chatbot with Touch and Graphics: An Interaction of Users for Emotional Expression and Turn-taking
CUI '20: Proceedings of the 2nd Conference on Conversational User InterfacesUse of chatbots for emotional exchange is recently increasing in various domains. However, as existing chatbots have been considered in terms of natural language processing techniques for interaction with text-based chatting, chatbot interaction with ...
Comments