The Role of Decision Authority and Stated Social Intent as Predictors of Trust in Autonomous Robots

Introduction to the Emerging Cognitive Science of Distributed Human-Autonomy Teams

Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore et al., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance.

Responsible (use of) AI

Although there is a rich history of philosophical definitions of ethics when applied to human behavior, applying the same concepts and principles to AI may be fraught with problems. Anthropomorphizing AI to have characteristics such as "ethics" may promote a dangerous, unrealistic expectation that AI can be trained to have inherent, guaranteed ethical behavior. The authors instead advocate for increased research into the ethical use of AI from initial ideation and design through operational use and sustainment. The authors advocate for five key research areas: (1) education in ethics and core AI concepts for AI developers, leaders, and users, (2) development and use of model cards or datasheets for datasets to provide transparency into the strengths, limits, and potential biases of a trained model, (3) employing human-centered design that seeks to understand human value structures within a task context and enable effective human-machine interaction through intuitive and transparent interfaces, (4) targeted use of run time assurance that monitors and modifies the inputs or outputs of a trained model when necessary to enforce ethical principles such as safety or limiting bias, and (5) developing best practices for the use of a joint human-AI co-creation and training experience to enable a shared mental model and higher performance through potential emergent behavior.

Human-AI teams-Challenges for a team-centered AI at work

As part of the Special Issue topic "Human-Centered AI at Work: Common Ground in Theories and Methods," we present a perspective article that looks at human-AI teamwork from a team-centered AI perspective, i. e., we highlight important design aspects that the technology needs to fulfill in order to be accepted by humans and to be fully utilized in the role of a team member in teamwork. Drawing from the model of an idealized teamwork process, we discuss the teamwork requirements for successful human-AI teaming in interdependent and complex work domains, including e.g., responsiveness, situation awareness, and flexible decision-making. We emphasize the need for team-centered AI that aligns goals, communication, and decision making with humans, and outline the requirements for such team-centered AI from a technical perspective, such as cognitive competence, reinforcement learning, and semantic communication. In doing so, we highlight the challenges and open questions associated with its implementation that need to be solved in order to enable effective human-AI teaming.