The objective is to propose three quantitative models of trust in automation.
Background:
Current trust-in-automation literature includes various definitions and frameworks, which are reviewed.
Method:
This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human–system interaction design.
Results:
The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application.
Conclusion:
The resulting models can be applied to provide quantitative trust measures in future experiments or system designs.
Applications:
Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.
CastelfranchiC.FalconeR. (2010). Trust theory: A socio-cognitive and computational model. New York, NY: Wiley.
2.
ChanceyE. T.BlissJ. P.ProapsA. B.MadhavenP. (2015). The role of trust as mediator between system characteristics and response behavior. Human Factors, 57, 947–958.
3.
CohenH. S.FerrellW. R. (1969). Human operator decision-making in manual control. IEEE Transactions on Man-Machine Systems, MMS-10, 41–47.
4.
FerrellW. R. (1965). Remote manipulation with transmission delay. IEEE Transactions on Human Factors in Electronics, HFE-4(1), 1–16
5.
GaoJ.LeeJ. D. (2006). Extending the decision field theory to model operators’ reliance on automation in supervisory control situations. IEEE Transactions on System, Man and Cybernetics, 36, 943–959.
6.
HancockP. A.BillingsD. R.SchaeferK. E.ChenJ. Y.DeVisserE. J.ParasuramanR. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53, 517–527.
7.
HoffK. A.BashirM. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human Factors, 57, 407–434.
8.
HoffmanR. R. (2018). A taxonomy of emergent trusting in the human–machine relationship. In SmithP. J.HoffmanR. R. (Eds.), Cognitive systems engineering: The future for a changing world (pp. 137–164). Boca Raton, FL: CRC Press.
9.
HoffmanR. R.LeeJ. D.WoodsD. D.ShadboltN.MillerJ.BradshawJ. M. (2009). The dynamics of trust in cyberdomains. IEEE Intelligent Systems, 24(6), 5–11.
10.
KalmanR. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering, 82D, 33–45.
11.
KleinmanD. L.BaronS.LevisonW. H. (1971). A control theoretic approach to manned vehicle system analysis. IEEE Transactions on Automatic Control, AC-16, 824–832.
12.
LeeJ. D.MorayN. (1992). Trust, control strategies and allocation of function in human–machine systems. Ergonomics, 35, 1243–1270.
13.
LeeJ. D.SeeK. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46, 50–80.
14.
LyonsJ. B.StokesC. K.EschelmanK. J.AlarconG. M.BarelkaA. J. (2011). Trustworthiness and IT suspicion: An evaluation of the nomological network. Human Factors, 53, 219–229.
15.
MayerR. C.DavisJ. H.SchoormanF. D. (1995). An integrative model of organized trust. Academy of Management Review, 20, 709–734.
16.
McBrideM.MorganS. (2010). Trust calibration for automated decision aids. Research brief, Institute for Homeland Security Solutions, Durham, NC.
17.
MendelM.SheridanT. B. (1989). Filtering information from human experts. IEEE Transactions on Systems, Man and Cybernetics, 36(1), 6–16.
18.
MuirB. M.MorayN. (1996). Trust in automation 2: Experimental studies of trust and human intervention in a process control simulation. Ergonomics, 39, 429–460.
19.
ParasuramanR.RileyV. (1997). Humans and automation: Misuse, disuse, abuse. Human Factors, 39, 230–253.
20.
SheridanT. B. (1972). On modeling performance of open-loop mechanisms. In On theory and practice of robots and manipulators (pp. 300–311). Berlin, Germany: Springer.
21.
SheridanT. B. (1988). Trustworthiness of command and control systems. In Proceedings of the IFAC/IFIP/IEA/IFORS Conference on Man–Machine Systems (pp. 427–431). Elmsford, NY: Pergamon.
22.
SheridanT. B. (2017). Modeling human–system interaction. New York, NY: Wiley.
23.
SheridanT. B. (in press). Individual differences in attributes of trust in automation: Measurement and application to system design. Lausanne, Switzerland: Frontiers Media.
24.
SheridanT. B.FerrellW. R. (1974). Man–machine systems. Cambridge, MA: MIT Press.
25.
StarrC. (1969). Social benefit versus technological risk. Science, 165, 1232–1238.
26.
YehM.WickensC. (2001). Display sampling in augmented reality: Effects of cue reliability and image realism on attention allocation and trust calibration. Human Factors, 43, 355–365.
