Factors Influencing the Acceptance of Artificial Intelligence

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, vvajratiya@gmail.com

Artificial intelligence (AI) adoption is impacted by a number of variables that fall under the categories of technology, society, ethics, and regulations. Understanding these variables is essential for the integration of artificial intelligence technology across many industries. The following are important factors that affect the acceptance of AI:

1. Performance and Accuracy

Acceptance of AI applications is highly influenced by their accuracy. AI solutions that continuously produce dependable and high-performing outcomes have a greater chance of being adopted by users [1].

2. Transparency and Explainability

AI algorithms should be transparent and that their decision-making procedures Users who understand how and why certain decisions are made are more inclined to adopt AI, especially in crucial industries like healthcare and finance.

3. Reliability and Trust

Acceptance of AI is heavily dependent on trust. Users must have faith that AI systems will function as anticipated and won’t jeopardise security or safety [1].

4. User Experience (UX) and Usability

The acceptability of AI is influenced by the user experience. Users are more likely to embrace and accept AI applications if they are clear, easy to use, and seamlessly integrated into current workflows [3] .

5. Perceived Benefits

When users see real benefits, they are more inclined to embrace AI. The perceived value of artificial intelligence (AI) is a motivating element, regardless of the benefits it offers—such as increased productivity, lower expenses, better decision-making [4].

6. Ethical Considerations

Acceptance of AI may be impacted by ethical issues like bias, justice, and accountability. AI systems that follow moral standards and norms have a higher chance of being adopted by users and stakeholders [5].

7. Cost and Accessibility

Acceptance may be impacted by the price of putting AI solutions into practice as well as how widely accessible they are to consumers. AI solutions that are accessible and reasonably priced have a greater chance of being adopted by a variety of industries [1].

To sum up, understanding and addressing these issues is critical to fostering an environment that supports the adoption and integration of AI technology into various aspects of society. Furthermore, the ethical development and deployment of AI can be supported by ongoing cooperation and discussion among relevant parties.


  1. Choung, H., David, P., & Ross, A. (2023). Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739.
  2. Theis, S., Jentzsch, S., Deligiannaki, F., Berro, C., Raulf, A. P., & Bruder, C. (2023, July). Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work. In International Conference on Human-Computer Interaction (pp. 355-380). Cham: Springer Nature Switzerland.
  3. Bingley, W. J., Curtis, C., Lockey, S., Bialkowski, A., Gillespie, N., Haslam, S. A., … & Worthy, P. (2023). Where is the human in human-centered AI? Insights from developer priorities and user experiences. Computers in Human Behavior, 141, 107617.
  4. Del Giudice, M., Scuotto, V., Orlando, B., & Mustilli, M. (2023). Toward the human–centered approach. A revised model of individual acceptance of AI. Human Resource Management Review, 33(1), 100856.
  5. Praveen, S. V., & Vajrobol, V. (2023). Can ChatGPT be Trusted for Consulting? Uncovering Doctor’s Perceptions Using Deep Learning Techniques. Annals of Biomedical Engineering, 1-4.
  6. Zamzami, I. F., Pathoee, K., Gupta, B. B., Mishra, A., Rawat, D., & Alhalabi, W. (2022). Machine learning algorithms for smart and intelligent healthcare system in Society 5.0. International Journal of Intelligent Systems37(12), 11742-11763.
  7. Chui, K. T., Gupta, B. B., Torres-Ruiz, M., Arya, V., Alhalabi, W., & Zamzami, I. F. (2023). A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition. Electronics12(8), 1915.
  8. Chaudhary, P., Gupta, B., & Singh, A. K. (2022). Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks. Telecommunication Systems81(1), 23-39.
  9. Colace, F., Guida, C. G., Gupta, B., Lorusso, A., Marongiu, F., & Santaniello, D. (2022, August). A BIM-based approach for decision support system in smart buildings. In Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 1 (pp. 471-481). Singapore: Springer Nature Singapore.
  10. Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.
  11. Bhushan, K., & Gupta, B. B. (2017). Security challenges in cloud

Cite As:

Vajrobol V. (2024) Factors Influencing the Acceptance of Artificial Intelligence, Insights2Techinfo, pp.1

67130cookie-checkFactors Influencing the Acceptance of Artificial Intelligence
Share this:

Leave a Reply

Your email address will not be published.