Designing Authoritative Presence in Social Robots
Why this work is in the frame
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Bibliographic record
Abstract
We define authoritative presence as letting people experience authority through human-made technology in sensory or non-sensory ways. Our goal is to design a robot that creates the impression of possessing capabilities worthy of respect as a source of authority, thereby enhancing compliance without attributing that authority to external sources, such as a specific person or organization. We hypothesized that strategies commonly used in the Wizard-of-Oz method could help manipulate authoritative presence as it strives to ensure that the robot is not perceived as having additional abilities beyond those introduced by the manipulations. Wizards typically need to maintain the robot’s functionality and abilities at an appropriate level to minimize unwanted influence on participants’ perceptions and interactions. By interviewing HRI researchers who have wizarded, we summarized their usual strategies and implemented the opposite behaviors in a robot to investigate if this would contribute to authoritative presence. Based on the findings, we designed four behaviors that include (1) let the robot have an open-ended conversation with people, (2) randomize the robot’s reaction delay timing, (3) let the robot move with inconsistent velocity, and (4) let the robot perceive people’s status without looking at them. To evaluate the impact of these behaviors, we conducted a video-based online experiment with 942 participants, using a between-subjects design. The experiment aimed to determine whether the behaviors conveying authoritative presence would make people perceive the robot as having more authority and increase their likelihood of complying with its requests. A mediation analysis indicated that despite a decrease in perceived authority, the imply authoritative presence condition had a positive effect on participant compliance. Our study formally introduces the concept of authoritative presence, providing a proof-of-concept for how robots can create authoritative presence through specific behaviors. This work lays the groundwork for future research on authority and robotics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it