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Adaptable Automation Transparency: Should Humans Be Provided Flexibility to Self-Select Transparency Information?

Monica Tatasciore1, Laura Bennett1, Vanessa K Bowden1

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Summary
This summary is machine-generated.

Allowing operators to self-select automation transparency (adaptable transparency) did not improve automation use accuracy. Higher fixed transparency, however, improved accuracy and reduced decision time in prior analyses.

Keywords:
adaptable automation transparencydecision riskdecision support aidshuman–automation teaminguninhabited vehicle control

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Area of Science:

  • Human-Computer Interaction
  • Automation and Control Systems
  • Cognitive Psychology

Background:

  • Increased automation transparency generally enhances accuracy but may increase automation bias.
  • Adaptable transparency aims to improve automation use by increasing the perceived value of high-transparency information.

Purpose of the Study:

  • To investigate if adaptable transparency improves automation use accuracy compared to fixed transparency levels.
  • To examine factors influencing the selection of higher transparency, such as decision risk and perceived difficulty.

Main Methods:

  • Two studies utilized an uninhabited vehicle (UV) management task with manipulated automation transparency (fixed low, fixed high, adaptable) and decision risk.
  • Participants selected the optimal UV for missions, with automation providing advice that was not always correct.

Main Results:

  • Adaptable transparency was selected for 41% of missions, more often for perceived difficult tasks; decision risk did not influence selection.
  • Fixed transparency did not improve automation use accuracy but reduced decision times.
  • Adaptable transparency did not yield better automation use outcomes than fixed transparency.

Conclusions:

  • No evidence was found that adaptable transparency enhances automation use.
  • Aggregated data suggest higher fixed transparency improves accuracy, reduces decision time, workload, and increases trust in automation.
  • Findings contribute to understanding optimal automation transparency design in workplaces.