Human Perception of Trust in AI-Assisted Communication Systems

  • Authors

    • Michael Rabin Professor, Hebrew University of Jerusalem, Israel. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-2024PII5P2H

    Published 07-05-2024

  • Artificial Intelligence, Trustworthy Ai, Human Perception, Ai-Assisted Communication, Explainable Ai, Human-Computer Interaction, Digital Communication Systems, User Trust, Ethical Ai, Communication Technology

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    M. Rabin, “Human Perception of Trust in AI-Assisted Communication Systems”, IJIRHT, vol. 7, no. 2, pp. 01–16, Jul. 2024, doi: 10.67228/3071561X/IJIRHT-2024PII5P2H.

  • Abstract

    Artificial Intelligence (AI) has transformed modern communication systems by enabling intelligent interactions, automated content generation, personalized recommendations, real-time translation, and conversational support. The widespread adoption of AI in areas such as social media, healthcare, education, customer service, and enterprise communication depends largely on user trust. Trust in AI communication systems is influenced by factors such as transparency, reliability, explainability, privacy, fairness, and accuracy. However, concerns regarding bias, misinformation, data privacy, and lack of explainability can reduce user confidence. This study examines technological, psychological, and social factors affecting trust in AI-based communication systems. The findings reveal that transparency, reliability, privacy protection, and explainability are critical for building long-term user trust. The study concludes that trust is a multidimensional concept involving technical performance, ethical considerations, and user experience, highlighting the need for trustworthy AI systems to support effective and responsible digital communication.

  • References

    [1] D. H. McKnight, V. Choudhury, and C. Kacmar, “Developing and validating trust measures for e-commerce: An integrative typology,” Information Systems Research, vol. 13, no. 3, pp. 334–359, Sept. 2002.

    [2] J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004.

    [3] B. Shneiderman, “Human-centered artificial intelligence: Reliable, safe and trustworthy,” International Journal of Human–Computer Interaction, vol. 36, no. 6, pp. 495–504, 2020.

    [4] A. Madsen and S. Gregor, “Measuring human-computer trust,” in Proceedings of the 11th Australasian Conference on Information Systems, Brisbane, Australia, 2000, pp. 6–8.

    [5] R. Hoff and M. Bashir, “Trust in automation: Integrating empirical evidence on factors that influence trust,” Human Factors, vol. 57, no. 3, pp. 407–434, 2015.

    [6] D. Gunning, “Explainable Artificial Intelligence (XAI),” Defense Advanced Research Projects Agency (DARPA), Program Report, 2017.

    [7] F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv preprint arXiv:1702.08608, 2017.

    [8] A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI),” IEEE Access, vol. 6, pp. 52138–52160, 2018.

    [9] T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence, vol. 267, pp. 1–38, 2019.

    [10] L. Floridi and J. Cowls, “A unified framework of five principles for AI in society,” Harvard Data Science Review, vol. 1, no. 1, pp. 1–15, 2019.

    [11] V. Dignum, Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Cham, Switzerland: Springer, 2019.

    [12] B. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, “The ethics of algorithms: Mapping the debate,” Big Data & Society, vol. 3, no. 2, pp. 1–21, 2016.

    [13] S. Wachter, B. Mittelstadt, and L. Floridi, “Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation,” International Data Privacy Law, vol. 7, no. 2, pp. 76–99, 2017.

    [14] European Commission High-Level Expert Group on Artificial Intelligence, Ethics Guidelines for Trustworthy AI. Brussels, Belgium: European Commission, 2019.

    [15] N. Luhmann, Trust and Power. Chichester, U.K.: Wiley, 1979.

    [16] R. R. Mahajan, S. Choudhary, and P. Sharma, “User trust in AI-based conversational systems: A systematic review,” IEEE Access, vol. 11, pp. 45672–45689, 2023.

    [17] A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, vol. 1, no. 9, pp. 389–399, 2019.

    [18] K. Siau and W. Wang, “Building trust in artificial intelligence, machine learning, and robotics,” Cutting-Edge Technologies in Higher Education, vol. 17, pp. 47–53, 2018.

    [19] IBM Research, “Explainable AI for trustworthy decision making,” IBM Journal of Research and Development, vol. 64, no. 2/3, pp. 1–15, 2020.

    [20] A. Rai, “Explainable AI: From black box to glass box,” Journal of the Academy of Marketing Science, vol. 48, no. 1, pp. 137–141, 2020.

    [21] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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