The comprehensive review synthesizes 64 empirical studies on communication and transactive memory systems (TMS). The results reveal that (a) a TMS forms through communication about expertise; (b) as a TMS develops, communication to allocate information and coordinate retrieval increases, promoting information exchange; and (c) groups update their TMS through communicative learning. However, direct interpersonal communication is not necessary for TMS development or utilization. Nor do high-quality information-sharing processes always occur within developed TMS structures. For future research, we propose a multidimensional network approach to TMS that incorporates technologies, addresses member characteristics, considers multiple communication types, and situates groups in context.

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis -Systematic Representative Design (SRD)concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.

As journalism has grappled with the potentials and boundaries of AI within the industry, journalists have produced plentiful articles detailing experimentation and potential consequences of AIdriven journalism (see, GPT-33, 2020). Accordingly, this article analyzes media coverage (N ¼ 95 articles) of AI in journalism over a 5-year period, starting in 2016 and ending in 2020, to examine prominent themes related to uses, roles, and concerns regarding AI in the newsroom. We sample coverage from 20 US and UK news media outlets representing a diversity of media with regards to media type and partisan leaning. We employ a thematic analysis on the media coverage of AI as it relates specifically to its use and application in journalism. Our exploration uncovers a tension between the industry and profession of journalism in highlighting the hopes and pitfalls of AI. It also allows for a discussion on assessing the place of AI in news making, especially with regard to the economic and contextual complexity in which news stories operate and the normative ideals of journalism in the digital era.

Implementing equity principles in resource allocation is challenging. In one approach, some US states implemented race-based prioritisation of COVID-19 vaccines in response to vast racial inequities in COVID-19 outcomes, while others used place-based allocation. In a nationally representative survey of n=2067 US residents, fielded in mid-April 2021 (before the entire US population became eligible for vaccines), we explored the public acceptability of race-based prioritisation compared with place-based prioritisation, by offering vaccines to harder hit zip codes before residents of other zip codes. We found that in general, a majority of respondents supported the place-based approach, and a substantial proportion supported the race-based plan. Support was higher among Democrats compared with Republicans. All US residents became eligible for vaccines on 19 April 2021 but as of this writing, equitable uptake of vaccines remains urgent not only for first doses for adults but also for boosters and for children. Our findings also provide a benchmark for future pandemic planning that racial and social justice in vaccine allocation are salient considerations for the public. The findings may furthermore be of interest to policy makers designing vaccine allocation frameworks in countries with comparable health disparities across social, ethnic and racial groups, and more broadly, for those exploring ways of promoting equity in resource allocation outside of a pandemic setting.

Media exists as the primary route through which the public learns about new technologies and thus plays an important role in shaping public sentiments. This article examines the influence of news media partisanship on the coverage of the controversial artificial intelligence (AI) technology facial recognition. A mixed-methods content analysis of news articles ( N = 451) from 23 US-based news outlets highlights the emergence of several frames in coverage of facial recognition pertaining to issues of privacy and surveillance, bias, technology’s ability to provide solutions, and its problematic development and implementation. Coverage was differentiated by partisanship, whereby left-leaning media focused more on ethical problems associated with the technology compared to their right-leaning peers who highlighted its abuses by foreign governments. Right-leaning media also referred more to technology’s positive uses, such as helping law enforcement, compared to left-leaning media. Finally, AI companies were the most dominant suppliers of information to the media regarding the technology.