Name
Abstract | ||
|---|---|---|
ABSTRACTIn this work, we investigate and model how human trust affects monitoring. We present a web-based human subject study in which the robot is a worker and the human plays the role of a supervisor. First, we evaluate the correlation between the human trust and monitoring by using statistical tests, and then we learn probabilistic models of the behavioral data collected through our user studies. These models can provide us with the likelihood of a human user monitoring a system given their level of trust. Such models can be leveraged in many systems including the ones designed to be resilient to automation bias and complacency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.5555/3523760.3523947 | ACM/IEEE International Conference on Human-Robot Interaction |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahra Zahedi | 1 | 0 | 0.68 |
| Sarath Sreedharan | 2 | 30 | 9.83 |
| Mudit Verma | 3 | 0 | 1.01 |
| Subbarao Kambhampati | 4 | 3453 | 450.74 |