Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

Collaborations among researchers and across disciplinary, organizational, and cultural boundaries are vital to address increasingly complex challenges and opportunities in science and society. In addition, unprecedented technological advances create new opportunities to capitalize on a broader range of expertise and information in scientific collaborations. Yet rapid increases in the demand for scientific collaborations have outpaced changes in the factors needed to support teams in science, such as institutional structures and policies, scientific culture, and funding opportunities. The Science of Team Science (SciTS) field arose with the goal of empirically addressing questions from funding agencies, administrators, and scientists regarding the value of team science (TS) and strategies for successfully leading, engaging in, facilitating, and supporting science teams. Closely related fields have rich histories studying teams, groups, organizations, and management and have built a body of evidence for effective teaming in contexts such as industry and the military. Yet few studies had focused on science teams. Unique contextual factors within the scientific enterprise create an imperative to study these teams in context, and provide opportunities to advance understanding of other complex forms of collaboration. This review summarizes the empirical findings from the SciTS literature, which center around five key themes: the value of TS, team composition and its influence on TS performance, formation of science teams, team processes central to effective team functioning, and institutional influences on TS. Cross-cutting issues are discussed in the context of new research opportunities to further advance SciTS evidence and better inform policies and practices for effective TS. (PsycINFO Database Record

Understanding how teams function is vital because they are increasingly dominating the production of high-impact science.

This essay discusses interdisciplinary research in the context of science policy and the practice of science. Comparisons between interdisciplinary research and other forms of cross-disciplinary research are made, and a brief discussion of the development of the concept of interdisciplinarity is provided. The overarching thesis of this essay is that interdisciplinary research is team research, that is, research conducted by a team. This notion is developed via recent policy discussions of team science and the need to understand interdisciplinary research in action. The author shows how it may be possible to consider the implementation of principles from teamwork and team training to improve interdisciplinary research and the practice of team science.

Objective: This article presents a model for predicting complex collaborative processes as they arise in one-of-a-kind problem-solving situations to predict performance outcomes. The goal is to outline a set of key processes and their interrelationship and to describe how these can be used to predict collaboration processes embedded within problem-solving contexts. Background: Teams are increasingly called upon to address complex problem-solving tasks in novel situations. This represents a domain of performance that to date has been underrepresented in the research literature. Method: Multidisciplinary theoretical and empirical literature relating to knowledge work in teams is synthesized. Results: A set of propositions developed to guide research into how teams externalize cognition and build knowledge in service of problem solving is presented. First, a brief overview of macrocognition in teams is provided to distinguish the present work from other views of team cognition. Second, a description of the foundational theoretical concepts driving the theory of macrocognition in teams presented here is provided. Third, a set of propositions described within the context of a model of macrocognition in teams is forwarded. Conclusion: The theoretical framework described in this article provides a set of empirically testable propositions that can ultimately guide practitioners in efforts to support macrocognition in teams. Application: A theory of macrocognition in teams can provide guidance for the development of training interventions and the design of collaborative tools to facilitate knowledge-based performance in teams.

Knowledge integration in diverse teams depends on their integrative capacity—the social and cognitive processes, along with emergent states, that shape a team’s ability to combine diverse knowledge. We argue that integrative capacity represents the potential that a team has to overcome various compositional, team, and contextual barriers to generating integrated and novel knowledge. This article focuses specifically on the unique challenges facing diverse science teams that have the goal of generating novel knowledge at the intersection of disciplinary, practice, and organizational boundaries. The integrative capacity of a science team is argued to help facilitate the social and cognitive integration processes necessary for effective team processes that enhance the likelihood of innovative team outcomes. Implications of our theoretical framework for practice and research on fostering innovation in diverse science teams are discussed.