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This edited volume reports on the growing body of research in science communication training and identifies best practices for communication training programs around the world. Theory and Best Practices in Science Communication Training provides a critical overview of this emerging field. It analyzes the role of communication training in supporting scientists' communication and engagement goals, including their motivations to engage in training, the design of training programs, methods for evaluation, and frameworks to support the role of communication training in helping scientists reach their goals. Overall, this collection reflects on the growth of the field and provides direction for developing future researcher-practitioner collaborations. With contributions from researchers and practitioners from around the world, this book will be of great interest to students, scholars and professionals within this emerging field.

Using data from a nationally representative survey of U.S. adults, this study explores how trust in key actors to responsibly manage artificial intelligence (AI) develops among members of the U.S. population and how trust, along with other key factors, shapes public attitudes toward AI. Greater trust is linked to stronger support for AI, both directly and indirectly (through risk and benefit perceptions). Furthermore, the strength or direction of the link between trust and support—as well as media diets and trust—differs significantly for liberals and conservatives, suggesting that Americans are indeed beginning to process AI-related information through a political lens.

The use of artificial intelligence-based algorithms for the curation of news content by social media platforms like Facebook and Twitter has upended the gatekeeping role long held by traditional news outlets. This has caused some US policymakers to argue that platforms are skewing news diets against them, and such claims are beginning to take hold among some voters. In a nationally representative survey experiment, we explore whether traditional models of media bias perceptions extend to beliefs about algorithmic news bias. We find that partisan cues effectively shape individuals' attitudes about algorithmic news bias but have asymmetrical effects. Specifically, whereas in-group directional partisan cues stimulate bias perceptions for members of both parties, Democrats, but not Republicans, also respond to out-group cues. We conclude with a discussion about the implications for the formation of attitudes about new technologies and the potential for polarization.