Exploring Trust Barriers to Future Autonomy: A Qualitative Look

  • Joseph B. LyonsEmail author
  • Nhut T. Ho
  • Anna Lee Van Abel
  • Lauren C. Hoffmann
  • W. Eric Fergueson
  • Garrett G. Sadler
  • Michelle A. Grigsby
  • Amy C. Burns
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 591)


Autonomous systems dominate future Department of Defense (DoD) strategic perspectives, yet little is known regarding the trust barriers of these future systems as few exemplars exist from which to appropriately baseline reactions. Most extant DoD systems represent “automated” versus “autonomous” systems, which adds complexity to our understanding of user acceptance of autonomy. The trust literature posits several key trust antecedents to automated systems, with few field applications of these factors into the context of DoD systems. The current paper will: (1) review the trust literature as relevant to acceptance of future autonomy, (2) present the results of a qualitative analysis of trust barriers for two future DoD technologies (Automatic Air Collision Avoidance System [AACAS]; and Autonomous Wingman [AW]), and (3) discuss knowledge gaps for implementing future autonomous systems within the DoD. The study team interviewed over 160 fighter pilots from 4th Generation (e.g., F-16) and 5th Generation (e.g., F-22) fighter platforms to gauge their trust barriers to AACAS and AW. Results show that the trust barriers discussed by the pilots corresponded fairly well to the existing trust challenges identified in the literature, though some nuances were revealed that may be unique to DoD technologies/operations. Some of the key trust barriers included: concern about interference during operational requirements; the need for transparency of intent, function, status, and capabilities/limitations; concern regarding the flexibility and adaptability of the technology; cyber security/hacking potential; concern regarding the added workload associated with the technology; concern for the lack of human oversight/decision making capacity; and doubts regarding the systems’ operational effectiveness. Additionally, the pilots noted several positive aspects of the proposed technologies including: added protection during last ditch evasive maneuvers; positive views of existing fielded technologies such as the Automatic Ground Collision Avoidance System; the potential for added operational capabilities; the potential to transfer risk to the robotic asset and reduce risk to pilots; and the potential for AI to participate in the entire mission process (planning-execution-debriefing). This paper will discuss the results for each technology and will discuss suggestions for implementing future autonomy into the DoD.


Trust Automation Autonomy Military 


  1. 1.
    Veloso, M., Aisen, M., Howard, A., Jenkins, C., Mutlu, B., Scassellati, B.: WTEC Panel Report on Human-Robot Interaction Japan, South Korea, and China. World Technology Evaluation Center, Inc., Arlington (2012)Google Scholar
  2. 2.
    Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrated model of organizational trust. Acad. Manag. Rev. 20, 709–734 (1995)Google Scholar
  3. 3.
    Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors 46, 50–80 (2004)CrossRefGoogle Scholar
  4. 4.
    Chen, J.Y.C., Barnes, M.J.: Human-agent teaming for multirobot control: a review of the human factors issues. IEEE Trans. Hum.-Mach. Syst. 44(1), 13–29 (2014)CrossRefGoogle Scholar
  5. 5.
    Hoff, K.A., Bashir, M.: Trust in automation: integrating empirical evidence on factors that influence trust. Hum. Factors 57, 407–434 (2015)CrossRefGoogle Scholar
  6. 6.
    Onnasch, L., Wickens, C.D., Li, H., Manzey, D.: Human performance consequences of stages and levels of automation: an integrated meta-analysis. Hum. Factors 56, 476–488 (2014)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Lyons, J.B., Saddler, G.G., Koltai, K., Battiste, H., Ho, N.T., Hoffmann, L.C., Smith, D., Johnson, W.W., Shively, R.: Shaping trust through transparent design: theoretical and experimental guidelines. In: Savage-Knepshield, P., Chen, J. (eds.) Advances in Human Factors in Robotics and Unmanned Systems, pp. 127–136. Springer, Cham (2017)CrossRefGoogle Scholar
  9. 9.
    Li, X., Hess, T.J., Valacich, J.S.: Why do we trust new technology? A study of initial trust formation with organizational information systems. J. Strateg. Inf. Syst. 17, 39–71 (2008)CrossRefGoogle Scholar
  10. 10.
    Guznov, S., Lyons, J.B., Nelson, A., Wooley, M.: The effects of automation error types on operators trust and reliance. In: Proceedings of HCI International, Toronto, CA (2016)Google Scholar
  11. 11.
    Pak, R., Fink, N., Price, M., Bass, B., Sturre, L.: Decision support aids with anthropomorphic characteristics influence trust and performance in younger and older adults. Ergonomics 55(9), 1–14 (2012)CrossRefGoogle Scholar
  12. 12.
    Merritt, S.M., Unnerstall, J.L., Lee, D., Huber, K.: Measuring individual differences in the perfect automation schema. Hum. Factors 57, 740–753 (2015)CrossRefGoogle Scholar
  13. 13.
    Lyons, J.B., Ho, N.T., Koltai, K., Masequesmay, G., Skoog, M., Cacanindin, A., Johnson, W.W.: A trust-based analysis of an air force collision avoidance system: test pilots. Ergon. Des. 24, 9–12 (2016)Google Scholar
  14. 14.
    Lyons, J.B.: Being transparent about transparency: a model for human-robot interaction. In: Sofge, D., Kruijff, G.J., Lawless, W.F. (eds.) Trust and Autonomous Systems: Papers from the AAAI Spring Symposium (Technical Report SS-13-07). AAAI Press, Menlo Park (2013)Google Scholar
  15. 15.
    Defense Science Board (DSB) Task Force on the Role of Autonomy in Department of Defense (DoD) Systems. Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics. Washington, DC (2012)Google Scholar
  16. 16.
    Defense Science Board (DSB) Summer Study on Autonomy. Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics. Washington, DC (2016)Google Scholar
  17. 17.
    Wadley, J., Jones, S.E., Stoner, D.E., Griffin, E.M., Swihart, D.E., Hobbs, K.L., Burns, A.C., Bier, J.M.: Development of an automatic air collision avoidance system for fighter aircraft. In: AIAA Infotech@Aerospace Conference, Guidance, Navigation, and Control and Co-located Conferences. Boston, MA (2013)Google Scholar
  18. 18.
    Jones, S.E., Petry, A.K., Eger, C.A., Turner, R.M., Griffin, E.M.: Automatic integrated collision system. In: 17th Australian Aerospace Congress. Melbourne, AU (2017)Google Scholar
  19. 19.
    Ho, N.T., Sadler, G.G., Hoffmann, L.C., Lyons, J.B., Fergueson, W.E., Wilkins, M.: A longitudinal field study of auto-GCAS acceptance and trust: first year results and implications. J. Cognit. Eng. Decis. Mak. (in press)Google Scholar

Copyright information

© Springer International Publishing AG (outside the USA) 2018

Authors and Affiliations

  • Joseph B. Lyons
    • 1
    Email author
  • Nhut T. Ho
    • 2
  • Anna Lee Van Abel
    • 1
  • Lauren C. Hoffmann
    • 2
  • W. Eric Fergueson
    • 1
  • Garrett G. Sadler
    • 2
  • Michelle A. Grigsby
    • 1
  • Amy C. Burns
    • 1
  1. 1.Air Force Research LaboratoryWright-Patterson AFBUSA
  2. 2.NVH Human Systems IntegrationCanoga ParkUSA

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