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.

This paper augments recent advances in social cognitive and affective neuroscience (SCAN) and illustrates their relevance to the development of novel human-machine systems. Advances in this area are crucial for understanding and exploring the social, cognitive, and neural processes that arise during human interactions with complex sociotechnological systems. Overviews of the major areas of SCAN research, including emotion, theory of mind, and joint action, are provided as the basis for describing three applications of SCAN to human-machine systems research and development. Specifically, this paper provides three examples to demonstrate the broad interdisciplinary applicability of SCAN and the ways it can contribute to improving a number of human-machine systems with the pursuit of further research in this vein. These include applying SCAN to learning and training, informing the field of human-robot interaction (HRI), and, finally, for enhancing team performance. The goal is to draw attention to the insights that can be gained by integrating SCAN with ongoing human-machine system research and to provide guidance to foster collaborations of this nature. Toward this end, we provide a systematic set of notional research questions for each detailed application within the context of the three major emphases of SCAN research. In turn, this study serves as a roadmap for preliminary investigations that integrate SCAN and human-machine system research.

In this paper, we introduce a cognitive agent architecture that can be used in the study of Human-Robot Interaction. The Cognitive Architecture for Perception-Reaction Intelligent Computer Agents (CAPRICA) is an extensible agent library built around the ideas of theory of mind, episodic memory, and embodied cognition. Existing agent research in each of these areas was used to formulate design requirements. We provide an overview of the library's design and discuss future work in progress.