Measuring trust in technology is a mainstay in Human Factors research. While trust may not perfectly predict reliance on technology or compliance with alarm signals, it is routinely used as a design consideration and assessment goalpost. Several methods of measuring trust have been employed in the past decades, but one self-report measure stands out due to its popular use, the Trust in Automated Systems Survey (Jian, Bisantz, & Drury, 2000). We conducted a study to assess whether the survey could create biased responses, and found evidence the original scale is in fact skewed toward positive ratings. Assessing the literature revealed the survey has been used in unaltered form across at least 100 different reports and remains frequently administered – therefore, the potential impact of this bias may be widespread. Future directions, considerations, and caveats for our assessment, and for using this scale, are discussed.
In future urban search and rescue teams, robots may be expected to conduct cognitive tasks. As the capabilities of robots change, so too will their interdependence with human teammates. Human factors and cognitive engineering are well-positioned to guide the design of autonomy for effective teaming. Previous work in the urban search and rescue synthetic task environment (USAR-STE) used Minecraft, a customizable gaming platform. In this effort, we advanced the USAR-STE by increasing interdependence in dyadic human-robot teaming through the Coactive Design framework. In this framework, we defined required capacities of victim identification in USAR from literature, and used them as inputs for modeling interdependence, and determined recommendations that would enhance interdependence in the task environment. Although Coactive Design is typically used to design interdependence for robots or jobs, we demonstrated how it can also be used to design an experimental team task environment.
This study focuses on methodological adaptations and considerations for remote research on Human-AI-Robot Teaming (HART) amidst the COVID-19 pandemic. Themes and effective remote research methods were explored. Central issues in remote research were identified, such as challenges in attending to participants' experiences, coordinating experimenter teams remotely, and protecting privacy and confidentiality. Instances of experimental design overcoming these challenges were identified in methods for recruitment and onboarding, training, team task scenarios, and measurement. Three case studies are presented in which interactive in-person testbeds for HART were rapidly redesigned to function remotely. Although COVID-19 may have temporarily constrained experimental design, future HART studies may adopt remote research methods to expand the research toolkit.
Autonomous robots have the potential to play a critical role in urban search and rescue (USAR) by allowing human counterparts of a response team to remain in remote, stable locations while the robots execute more dangerous work in the field. However, challenges remain in developing robot capabilities suitable for teaming with humans. Communicating effectively is one of these challenges, especially if plan deviations during field operations require robot explanation. A virtual USAR team task experiment was conducted in Minecraft with a confederate acting as the remote robot. Four explanation-based communication conditions were tested: (1) always explain-the robot automatically provided explanations for any off-plan behaviors, (2) explain if asked-the robot provided an explanation only when the human counterpart requests it, (3) pull prime-the same as (2) but participants also experienced implicit training to pull information from the robot, and (4) never explain-a baseline condition in which the robot acknowledged requests but would not provide an explanation. Results indicate that the training in (3) generated more team communication than (1), but this did not improve team performance or shared situation awareness. Rather, team performance and shared situation awareness was best supported by a moderate level of explanations and the robot pushing information. These findings reinforce the importance of designing robot communication strategies that can reduce human workload, particularly communication overhead, in dynamic and time-constrained tasks.
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