“Good” and “Bad” Machine Agency in the Context of Human-AI Communication: The Case of ChatGPT
Brandtzaeg, Petter Bae; You, Yukun; Wang, Xi; Lao, Yucong (2023-11-26)
Springer
26.11.2023
Brandtzaeg, P.B., You, Y., Wang, X., Lao, Y. (2023). “Good” and “Bad” Machine Agency in the Context of Human-AI Communication: The Case of ChatGPT. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_1
https://rightsstatements.org/vocab/InC/1.0/
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in HCI International 2023 – Late Breaking Papers: 25th International Conference on Human-Computer Interaction, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part VI. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-48057-7_1
https://rightsstatements.org/vocab/InC/1.0/
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in HCI International 2023 – Late Breaking Papers: 25th International Conference on Human-Computer Interaction, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part VI. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-48057-7_1
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202504022388
https://urn.fi/URN:NBN:fi:oulu-202504022388
Tiivistelmä
Abstract
Machine agency, defined as the ability of machines to act autonomously and interact with users, is becoming increasingly significant in the field of human-machine interaction research. This is especially evident in relation to ChatGPT and other generative artificial intelligence (AI) tools. In this paper, we present an initial extension of S. Shyam Sundar’s theory of machine agency, specifically in the context of human-AI communication. We review existing literature on this topic and use real-life news reports on ChatGPT from November 2022 to April 2023 as a basis for illustrating the factors that influence people’s perceptions of machine agency as either positive or negative. These perceptions are influenced by a range of factors, including ethical alignment, privacy, transparency, social inclusiveness, human autonomy, and well-being. We propose a more explicit differentiation between “good” and “bad” machine agency, a conceptualization that can enhance our understanding of the complexities of human-AI communication. We believe this approach can contribute to the development of guidelines and best practices for using generative AI tools and similar AI technologies. Finally, this conceptualization may help people and the public to better benefit from generative AI and identify its risks.
Machine agency, defined as the ability of machines to act autonomously and interact with users, is becoming increasingly significant in the field of human-machine interaction research. This is especially evident in relation to ChatGPT and other generative artificial intelligence (AI) tools. In this paper, we present an initial extension of S. Shyam Sundar’s theory of machine agency, specifically in the context of human-AI communication. We review existing literature on this topic and use real-life news reports on ChatGPT from November 2022 to April 2023 as a basis for illustrating the factors that influence people’s perceptions of machine agency as either positive or negative. These perceptions are influenced by a range of factors, including ethical alignment, privacy, transparency, social inclusiveness, human autonomy, and well-being. We propose a more explicit differentiation between “good” and “bad” machine agency, a conceptualization that can enhance our understanding of the complexities of human-AI communication. We believe this approach can contribute to the development of guidelines and best practices for using generative AI tools and similar AI technologies. Finally, this conceptualization may help people and the public to better benefit from generative AI and identify its risks.
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