The Impact of Public Privacy Concerns on the Acceptance of AI in Social Good Projects

Authors

  • Xinyi Li
  • Yinuo Song
  • Zhenxiang Zhu

DOI:

https://doi.org/10.54097/bfp6m518

Keywords:

Public Privacy, Social Good Projects, privacy calculus model, technology acceptance model, artificial intelligence.

Abstract

This study mainly explores the relationship between the public's privacy concerns and the acceptance of artificial intelligence technology in public welfare projects driven by artificial intelligence. According to the privacy calculus model (PCM) and the technology acceptance model (TAM), the study shows that the public's privacy concerns will indirectly affect the perceived usefulness of the technology by affecting the public's perceived risks, and ultimately affect the public's acceptance of artificial intelligence technology in public welfare projects. For example, in public welfare projects such as medical care, education, and environmental protection driven by artificial intelligence, concerns about privacy leakage often reduce public trust and acceptance, affecting the advancement of projects and the development of artificial intelligence technology. On the contrary, effective privacy protection measures and open and transparent information disclosure behaviors can alleviate these concerns and increase the public's willingness to accept. The results of the study provide a theoretical framework for understanding how the public's privacy concerns in social welfare projects affect technology acceptance, and provide a basis for technology developers and policymakers to better improve technology and policies. Finally, the study proposes a strategy to balance technology development and privacy protection and reduce perceived risks while enhancing perceived usefulness.

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References

[1] M. O. Khan, Towards Efficient and Responsible AI: Developing Resource-Efficient and Fair Deep Learning Models, Ph. D. thesis, New York University, Tandon School of Engineering (2024)

[2] N. Gillespie, S. Lockey, C. Curtis, J. Pool, A. Akbari, Trust in artificial intelligence: A global study. The University of Queensland and KPMG Australia, 10 (2023)

[3] J. Radhakrishnan, M. Chattopadhyay, Determinants and barriers of artificial intelligence adoption–A literature review. In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18-19, Proceedings, Part I. Springer International Publishing 89-99 (2020).

[4] S. A. Vannoy, P. Palvia, The social influence model of technology adoption. Communi. ACM, 53, 149-153 (2010)

[5] M. Nayak, J. Tiyadi, Social Welfare, and Artificial Intelligence's Role: A Comprehensive Summary of the Study. AI in the Social and Business World: A Comprehensive Approach, 32-66 (2024)

[6] U. Lindqvist, P. G. Neumann, The future of the Internet of Things. Communi. of the ACM, 60, 26-30 (2017)

[7] E. Attié, L. Meyer-Waarden, the acceptance and usage of smart connected objects according to adoption stages: an enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy calculus theories. Technol. Forecast. Soc. Change. 176, 121485 (2022)

[8] T. Dinev, P. Hart, An extended privacy calculus model for e-commerce transactions. Inf. Syst. Res. 17, 61-80. (2006)

[9] M. S. Featherman, P.A. Pavlou, Predicting e-services adoption: a perceived risk facets perspective. Int. J. Hum. Comput. Stud. 59, 451-474 (2003)

[10] E. M. Schomakers, C. Lidynia, M. Ziefle, the role of privacy in the acceptance of smart technologies: Applying the privacy calculus to technology acceptance. Int. J. Hum. Comput. Stud. 38, 1276-1289 (2022)

[11] P. Carey, Data protection: a practical guide to UK and EU law. Oxford University Press, Inc. (2018)

[12] M. Y. Shaheen, Applications of Artificial Intelligence (AI) in healthcare: A review. Sci. Open Preprints. (2021)

[13] W. Holmes, I. Tuomi, State of the art and practice in AI in education. Eur. J. Educ. 57, 542-570 (2022)

[14] Y. Himeur, B. Rimal, A. Tiwary, A. Amira, Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives. Inf. Fusion 86, 44-75 (2022)

[15] F. D. Davis, Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption. 205, 219 (1989)

[16] N. Marangunić, A. Granić, Technology acceptance model: a literature review from 1986 to 2013. Universal Access Inf. 14, 81-95. (2015)

[17] P. A. Pavlou, Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 7, 101-134. (2003)

[18] T. Dinev, P. Hart, An extended privacy calculus model for e-commerce transactions. Inf. Syst. Res. 17, 61-80 (2006)

[19] Y. Chen, P. Esmaeilzadeh, Generative AI in medical practice: In-depth exploration of privacy and security challenges J. Med. Internet Res. 26 (2024)

[20] Akamai. Cyber Attack Healthcare Report 2024. (2024)

[21] M. A. Dzhorobaeva, K. A. Mamadalieva, A. S. Kaliev, Ethical aspects of the use of AI in education: issues of confidentiality, fairness and transparency. (2025)

[22] R. Vilsack, Emerging Innovations in Data Transparency, Governance, and Quality. NSC. (2022)

[23] S. Madan, P. Kumar, S. Rawat, T. Choudhury, Analysis of weather prediction using machine learning & big data. In 2018 International conference on advances in computing and communication engineering (ICACCE) 259-264 (2018)

[24] C. Wang, L. Ma, Third-party monitoring and environmental data manipulation: the case of air quality control in Chinese cities. In Handbook on Climate Change and Environmental Governance in China 345-359 (2024)

[25] Ministry of Ecology and Environment. Citizen ecological environmental behavior survey report. Beijing: Center for Environmental and Economic Policy Research. (2022)

[26] P. Esmaeilzadeh, The impacts of the perceived transparency of privacy policies and trust in providers for building trust in health information exchange: an empirical study. JMIR Med. Inform. 7, e14050 (2019)

[27] Apple, App privacy details on the App Store. (2020) Retrieved from: https://support.apple.com/zh-cn/102399

[28] Statista, User opinion on Apple's new privacy policies in the United States. Statista. (2023) Retrieved from: https://www.statista.com/statistics/1224850/user-opinion-on-apple-s-new-privacy-policies-us/

[29] Time, what teenagers think about AI. (2024) Retrieved from: https://time.com/7098524/teenagers-ai-risk-lawmakers/

[30] Pew Research Center, U.S. adults under 30 have different foreign policy priorities than older adults. Pew Research Center. (2024) Retrieved from: https://www.pewresearch.org/short-reads/2024/07/19/us-adults-under-30-have-different-foreign-policy-priorities-than-older-adults/

[31] B. C. Stahl, D. Wright, Ethics and Privacy in AI and big data: Implementing responsible research and innovation. IEEE Secur. Priv. 16, 26-33 (2018)

[32] W. J. Von Eschenbach, Transparency and the black box problem: Why we do not trust AI. Philos. Technol. 34, 1607-1622 (2021)

[33] Google, AI Principles: Responsible AI for everyone. (n.d.) Retrieved from: https://ai.google/responsibility/principles/

[34] N. Tomašev, J. Cornebise, F. Hutter, S. Mohamed, A. Picciariello, B. Connelly, C. Clopath, AI for social good: unlocking the opportunity for positive impact. Nat. Commun. 11, 2468 (2020)

[35] A. El Ouadrhiri, A. Abdelhadi, Differential privacy for deep and federated learning: A survey. IEEE 10, 22359-22380 (2022)

[36] K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, H. V. Poor, Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454-3469 (2020)

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Published

25-03-2025