Enhancing autonomy transparency: an option-centric rationale approach
This addresses the human factors problem of trust and transparency in autonomous systems for users, but it is incremental as it builds on existing rationale display methods.
The study tackled the problem of low trust and sub-optimal performance in human-autonomy teams due to lack of transparency by proposing an option-centric rationale display, resulting in higher trust, faster trust calibration, and improved task performance among 34 participants in a game-based experiment.
While the advances in artificial intelligence and machine learning empower a new generation of autonomous systems for assisting human performance, one major concern arises from the human factors perspective: Humans have difficulty deciphering autonomy-generated solutions and increasingly perceive autonomy as a mysterious black box. The lack of transparency contributes to the lack of trust in autonomy and sub-optimal team performance. To enhance autonomy transparency, this study proposed an option-centric rationale display and evaluated its effectiveness. We developed a game Treasure Hunter wherein a human uncovers a map for treasures with the help from an intelligent assistant, and conducted a human-in-the-loop experiment with 34 participants. Results indicated that by conveying the intelligent assistant's decision-making rationale via the option-centric rationale display, participants had higher trust in the system and calibrated their trust faster. Additionally, higher trust led to higher acceptance of recommendations from the intelligent assistant, and in turn higher task performance.