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Challenges in Understanding Trust and Trust Modeling

Quenching the Thirst for AI Trust Management

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Transactions on Computational Science XL

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References

  1. Majority Staff of the Committee on Transportation and Infrastructure: The Design, Development & Certification of the Boeing 737 Max. Technical Report. D.C. House Committee on Transportation and Infrastructure, Washington (2020)

    Google Scholar 

  2. Helmore, E.: Tesla behind eight-vehicle crash was in ‘full self-driving’ mode, says driver, https://www.theguardian.com/technology/2022/dec/22/tesla-crash-full-self-driving-mode-san-francisco. Accessed 18 4 2023

  3. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine Bias, 1st edn., pp. 254–264. Auerbach Publications (2016)

    Google Scholar 

  4. Dastin, J.: Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women. 1st edn., pp. 296–299 Auerbach Publications (2018)

    Google Scholar 

  5. National Academies of Sciences, Engineering, and Medicine: Human-AI Teaming: State-of-the-Art and Research Needs. Technical report. Washington, DC: The National Academies Press (2022)

    Google Scholar 

  6. Hou, M., Ho, G., Dunwoody, D.: IMPACTS: a trust model for human-autonomy teaming. Hum.-Intell. Syst. Integr. 3(2), 79–97 (2021)

    Article  Google Scholar 

  7. Seshia, S.A., Sadigh, D., Sastry, S.S.: Toward Verified Artificial Intelligence. Commun. ACM 65(7), 46–55 (2022)

    Article  Google Scholar 

  8. Wing, J.M.: Trustworthy AI. Commun. ACM 64(10), 64–71 (2021)

    Google Scholar 

  9. Hou, M., et al.: Frontiers of brain-inspired autonomous systems: how does defense R&D drive the innovations? IEEE Syst. Man Cybernet. Mag. 8(2), 8–20 (2022)

    Article  Google Scholar 

  10. Lewicki, R.J., Tomlinson, E.C., Gillespie, N.: Models of interpersonal trust development: theoretical approaches, empirical evidence, and future directions. J. Manag. 32(6), 991–1022 (2006)

    Google Scholar 

  11. Atkinson, D.J., Clark, M.H.: Autonomous agents and human interpersonal trust: can we engineer a human-machine social interface for trust? Technical Report No. SS-13–07. AAAI Press (2013)

    Google Scholar 

  12. Barbalet, J.: The experience of trust: it’s content and basis. In: Masamichi, S. (eds.) Trust in Contemporary Society, vol. 42, pp. 11–30. Brill (2019)

    Google Scholar 

  13. Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709 (1995)

    Article  Google Scholar 

  14. Jøsang, A., Haller, J.: Dirichlet reputation systems. In: The Second International Conference on Availability, Reliability and Security (ARES 2007), pp. 112–119 (2007)

    Google Scholar 

  15. Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy computational models for trust and reputation systems. Electron. Commer. Res. Appl. 8(1), 37–47 (2009)

    Article  Google Scholar 

  16. Sheridan, T.B.: Individual differences in attributes of trust in automation: measurement and application to system design. Front. Psychol. 10(7), 1117 (2019)

    Article  Google Scholar 

  17. Kaplan, A.D., Kessler, T.T., Brill, J.C., Hancock, P.A.: Trust in artificial intelligence: meta-analytic findings. Hum. Factors 65(2), 337–359 (2021)

    Article  Google Scholar 

  18. Wubs-Mrozewicz, J.: The concept of language of trust and trustworthiness: (Why) history matters. J. Trust Res. 10(1), 91–107 (2020)

    Article  Google Scholar 

  19. Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., de Visser, E.J., Parasuraman, R.: A meta-analysis of factors affecting trust in human-robot interaction. Hum. Factors 53(5), 517–527 (2011)

    Article  Google Scholar 

  20. Hancock, P.A., Kessler, T.T., Kaplan, A.D., Brill, J.C., Szalma, J.L.: Evolving trust in robots: specification through sequential and comparative meta-analyses. Hum. Factors 63(7), 1196–1229 (2021)

    Article  Google Scholar 

  21. Hoff, K.A., Bashir, M.: Trust in automation: integrating empirical evidence on factors that influence trust. Hum. Factors 57(3), 407–434 (2015)

    Article  Google Scholar 

  22. Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors 46(1), 50–80 (2004)

    Article  Google Scholar 

  23. de Visser, E.J., et al.: Towards a theory of longitudinal trust calibration in human-robot teams. Int. J. Soc. Robot. 12(2), 459–478 (2020)

    Article  Google Scholar 

  24. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  25. Cox, E.: The fuzzy systems handbook: a practitioner’s guide to building and maintaining fuzzy systems. AP Professional (1994)

    Google Scholar 

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Hou, M. (2023). Challenges in Understanding Trust and Trust Modeling. In: Gavrilova, M., et al. Transactions on Computational Science XL. Lecture Notes in Computer Science(), vol 13850. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-67868-8_1

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  • DOI: https://doi.org/10.1007/978-3-662-67868-8_1

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