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Calibrating workers' trust in intelligent automated systems.

Gale M Lucas1, Burcin Becerik-Gerber2, Shawn C Roll3

  • 1USC Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA.

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This study reevaluates calibrated trust in automation, updating its definition for modern, intelligent systems. It emphasizes accurate trust judgments in the evolving automated workplace.

Keywords:
automationcalibrated trusttrustworkers

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Area of Science:

  • Human-Computer Interaction
  • Automation and Robotics
  • Organizational Psychology

Background:

  • The increasing prevalence of automation necessitates a critical understanding of trust in these systems.
  • Existing models of trust in automation were developed for less intelligent machines, limiting their applicability to current advanced technologies.
  • Modern automation exhibits enhanced intelligence and interactive capabilities, requiring a revised framework for trust calibration.

Purpose of the Study:

  • To reevaluate and update the general understanding of calibrated trust in automation.
  • To apply this updated understanding to the context of worker trust in workplace automation.
  • To incorporate the nuances of highly intelligent and interactive automated systems into trust models.

Main Methods:

  • Literature review of seminal trust in automation models.
  • Conceptual analysis of evolving automation capabilities and their impact on trust.
  • Theoretical revision of the concept of calibrated trust in automation.

Main Results:

  • Identified limitations of existing trust models for contemporary intelligent automation.
  • Proposed an updated definition of calibrated trust in automation that accommodates advanced AI and interactive systems.
  • Highlighted the need for new frameworks to assess and manage trust in human-automation collaboration.

Conclusions:

  • The concept of calibrated trust in automation requires revision to account for increasingly intelligent and interactive automated systems.
  • Accurate trust calibration is crucial for effective human-automation collaboration in the modern workplace.
  • Further research is needed to develop and validate new models for trust in advanced automation.