Calibrating workers' trust in intelligent automated systems

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.