User Factors and Trust in ChatGPT: Investigating the Relationship between Demographic Variables, Experience with AI Systems, and Trust in ChatGPT
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This study explores the relationship between various user factors and the level of trust in ChatGPT, a sophisticated language model exhibiting human-like capabilities. Specifically, we considered demographic characteristics such as age, education, gender, and major, along with factors related to previous AI experience, including duration, frequency, proficiency, perception, and familiarity. Through a survey of 140 participants, comprising 71 females and 69 males, we collected and analyzed the data to see how these user factors have a relationship with trust in ChatGPT. Both descriptive and inferential statistical methods, encompassing multiple linear regression models, were employed in our analysis. Our findings reveal significant relationships between user factors such as gender, the perception of prior AI interactions, self-evaluated proficiency, and Trust in ChatGPT. This research not only enhances our understanding of trust in artificial intelligence but also offers valuable insights for AI developers and practitioners in the field.
Abstract
This study explores the relationship between various user factors and the level of trust in ChatGPT, a sophisticated language model exhibiting human-like capabilities. Specifically, we considered demographic characteristics such as age, education, gender, and major, along with factors related to previous AI experience, including duration, frequency, proficiency, perception, and familiarity. Through a survey of 140 participants, comprising 71 females and 69 males, we collected and analyzed the data to see how these user factors have a relationship with trust in ChatGPT. Both descriptive and inferential statistical methods, encompassing multiple linear regression models, were employed in our analysis. Our findings reveal significant relationships between user factors such as gender, the perception of prior AI interactions, self-evaluated proficiency, and Trust in ChatGPT. This research not only enhances our understanding of trust in artificial intelligence but also offers valuable insights for AI developers and practitioners in the field.
참고문헌 (34)
- "Twitter" (2022)[URL]
- Cramer, H., "The effects of transparency on trust in and acceptance of a content-based art recommender", User Modeling and User-Adapted Interaction, 18(5), pp.455~496 (2008)
- Yin, M., "Understanding the effect of accuracy on trust in machine learning models", Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp.1~12 (2019)
- Yuan, S., "Trust in artificial intelligence in customer service: Factors influencing its formation and development", International Journal of Information Management, 49, pp.233~242 (2019)
- Rousseau, D. M., "Not so different after all: A cross-discipline view of trust", Academy of Management Review, 29(3), pp.393~404 (2004)
- Lee, J. D., "Trust in automation:Designing for appropriate reliance", Human Factors, 46(1), pp.50~80 (2004)
- Hancock, P. A., "A meta-analysis of factors affecting trust in human-robot interaction", Human Factors, 53(5), pp.517~527 (2011)
- Hoff, K. A., "Trust in automation : Integrating empirical evidence on factors that influence trust", Human Factors, 57(3), pp.407~434 (2015)
- Dzindolet, M. T., "The role of trust in automation reliance", International Journal of Human-Computer Studies, 58(6), pp.697~718 (2003)
- Brown, T. B., "Language models are few-shot learners", Neural Information Processing Systems, 33, pp.1877~1901 (2020)
- Radford, A., "Language models are unsupervised multitask learners", OpenAI Blog, 1(8), pp.9~ (2019)
- Luger, E., "Like having a really bad PA: The Gulf between user expectation and experience of conversational agents", Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp.5286~5297 (2016)
- Siau, K., "Building trust in artificial intelligence, machine learning, and robotics", Cutter Business Technology Journal, 31(2), pp.47~53 (2018)
- Følstad, A., "Chatbots and the new world of HCI Interactions", 24(4), pp.38~42 (2017)
- Cave, S., "The ethics of AI: Issues, developments, and recommendations", Proceedings of the 2018AAAI, 2018, 1~7
- 김효정, "ChatGPT의 특성이 사용의도에 미치는 영향에 관한 연구: 교사의 디지털 기술 조절효과를 중심으로", (사)디지털산업정보학회 논문지, 19(2), pp.135~145 (2023), (사)디지털산업정보학회
- Devadas Menon, "Chatting with ChatGPT: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model", Heliyon, 9(11), pp.127~143 (2023)
- Kreps S, "Exploring the artificial intelligence Trust paradox: Evidence from a survey experiment in the United States", PLOS ONE, 18(7) (2023)
- Gillath, O., "Attachment and trust in artificial intelligence", Computers in Human Behavior, 115, pp.106607~ (2021)
- Czaja, S. J., "Factors predicting the use of technology:Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE)", Psychology and Aging, 21(2), pp.333~352 (2006)
- Shandilya, E., "Understanding Older Adults’ Perceptions and Challenges in Using AI-enabled Everyday Technologies", ~ (2022)
- Gillespie, N., "Trust in Artificial Intelligence: A Global Study", The University of Queensland and KPMG Australia (2023)
- Hillesheim, A. J., "Relationships between User Demographics and User Trust in an Autonomous Agent", Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), pp.314~318 (2017)
- Kizilcec, R. F, "How much information?: Effects of transparency on trust in an algorithmic interface", Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp.2390~2395 (2016)
- Jackson, K. M., "Modeling the Impacts of Positive Interaction Frequency on Subjective Trust in an Autonomous Agent: A Linear Mixed Model Approach", Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 66(1), pp.798~801 (2022)
- Bach, T. A., "A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective", International Journal of Human–Computer Interaction, pp.1~16 (2022)
- Venkatesh, V., "User acceptance of information technology: Toward a unified view", MIS Quarterly, 27(3), pp.425~478 (2003)
- Sheridan, T. B., "Individual differences in attributes of trust in automation : Measurement and application to system design", Frontiers in Psychology, 10, pp.1117~ (2019)
- Muir, B. M., "Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems", Ergonomics, 37(11), pp.1905~1922 (1994)
- Parasuraman, R., "Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs", Journal of Cognitive Engineering and Decision Making, 2(2), pp.140~160 (2008)
- Castelfranchi, C., "Trust theory: A socio-cognitive and computational model", pp.147~190 (2010), John Wiley & Sons
- Jian, J. Y., "Foundations for an empirically determined scale of trust in automated systems", Int. J. Cogn. Ergon, 4, pp.53~71 (2000)
- Meinald T. Thielsch, "Facets of Website Content", Human-Computer Interaction, 34(4), pp.279~327 (2019)
- 박상우, "인공지능 역량 함양을 위한 경험학습 기반 교육에 관한 고찰", (사)디지털산업정보학회 논문지, 19(1), pp.153~172 (2023), (사)디지털산업정보학회