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Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams

  • Ewart J. de VisserEmail author
  • Marieke M. M. Peeters
  • Malte F. Jung
  • Spencer Kohn
  • Tyler H. Shaw
  • Richard Pak
  • Mark A. Neerincx
Article
  • 132 Downloads

Abstract

The introduction of artificial teammates in the form of autonomous social robots, with fewer social abilities compared to humans, presents new challenges for human–robot team dynamics. A key characteristic of high performing human-only teams is their ability to establish, develop, and calibrate trust over long periods of time, making the establishment of longitudinal human–robot team trust calibration a crucial part of these challenges. This paper presents a novel integrative model that takes a longitudinal perspective on trust development and calibration in human–robot teams. A key new proposed factor in this model is the introduction of the concept relationship equity. Relationship equity is an emotional resource that predicts the degree of goodwill between two actors. Relationship equity can help predict the future health of a long-term relationship. Our model is descriptive of current trust dynamics, predictive of the impact on trust of interactions within a human–robot team, and prescriptive with respect to the types of interventions and transparency methods promoting trust calibration. We describe the interplay between team trust dynamics and the establishment of work agreements that guide and improve human–robot collaboration. Furthermore, we introduce methods for dampening (reducing overtrust) and repairing (reducing undertrust) mis-calibrated trust between team members as well as methods for transparency and explanation. We conclude with a description of the implications of our model and a research agenda to jump-start a new comprehensive research program in this area.

Keywords

Relationship equity Social autonomy Trust repair Trust calibration Work agreements Agents Social abilities Human–robot interaction Collaboration Team 

Notes

Acknowledgements

This material is based upon work supported by the Air Force Office of Scientific Research under award numbers 16RT0881 and FA9550-18-1-0455, as well as the Dutch Ministry of Defence’s exploratory research program (project Human-AI Teaming).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Warfighter Effectiveness Research CenterUnited States Air Force AcademyColorado SpringsUSA
  2. 2.Department of PsychologyClemson UniversityClemsonUSA
  3. 3.George Mason UniversityFairfaxUSA
  4. 4.TNO, Perceptual and Cognitive SystemsDE SoesterbergThe Netherlands
  5. 5.Delft University of TechnologyDelftThe Netherlands
  6. 6.Department of Information ScienceCornell UniversityIthacaUSA

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