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

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.