The widespread adoption of artificial intelligence (AI)—particularly in “hidden” everyday applications—is creating a new and distinct form of digital inequality.

Targeting only a small group of well-connected individuals with smoking reduction interventions produces the largest reductions in smoking, researchers report. The findings provide actionable insights for designing social network-based interventions that leverage peer influence effectively. Peer influence plays a powerful role in shaping adolescent behavior, particularly in health-related habits such as smoking, as behaviors and norms spread through social networks during a developmentally sensitive period. Although prior research has shown that such influences can cascade through friendships and even friends-of-friends, how quickly the influence fades with social distance, and how social network structure affects that process, has remained unclear. Using Stochastic Actor-Oriented Models (SAOMS) and data from 3,154 students across two schools in the National Longitudinal Study of Adolescent to Adult Health (Add Health) dataset, Cheng Wang and colleagues investigated how peer influence decays across social distance. To better understand how behavioral effects spread and diminish, they evaluated the effects of different smoking reduction intervention strategies, targeting either random students or those most central within social networks. Wang et al. found that peer influence in the context of smoking reduction interventions extends up to three degrees of separation from intervention targets. Targeting 10 to 30% of highly connected individuals within a social network produced the greatest reductions in smoking, while increasing coverage beyond 40 to 50% yielded diminishing returns due to network saturation. Moreover, the authors discovered that social network structure mattered. For example, influence spread more widely and decayed more slowly in denser networks, while sparser networks showed more limited diffusion.

A long-standing scientific consensus holds that vaccines are not associated with autism. However, a recent shift in messaging by the U.S. Centers for Disease Control and Prevention (CDC) moved from emphasizing this consensus to highlighting uncertainty, potentially implying that a link has not been fully ruled out. Although this policy change could have significant consequences for public health, its effects on how people think, feel, and act regarding vaccination remain poorly understood. Moreover, moving from a clear, evidence-based consensus to messaging that emphasizes uncertainty may do more than influence vaccine decisions, it can also contribute to wider social effects, such as deepening political divisions, weakening trust in institutions, and making individuals more receptive to dismissing scientific evidence, especially in areas where experts largely agree. In this Policy Article, Bohm et al. conducted an online experiment using an opt-in sample of 2,989 U.S. adult participants. Participants were randomly exposed to either a consensus-based or uncertainty-based CDC-style statement, and their responses were measured. They found that exposure to the uncertainty-based message increased perceived vaccine risks and uncertainty, reduced vaccination intentions, lowered trust in the CDC, and increased endorsement of science-denial strategies. These effects occurred regardless of participants’ political orientation. “Overall, the findings underscore the responsibility of public health institutions to communicate scientific uncertainty in ways that protect, rather than inadvertently weaken, population health,” write the authors.

The scientific consensus is that vaccinations are neither causally nor statistically linked to autism. The US health authority CDC changed its official communication on this matter and instead emphasised a connection could not be scientifically ruled out. An international research team from Vienna, Erfurt, Hamburg, and Copenhagen, led by the University of Vienna, has now investigated the consequences of this. The results are clear: those who read the current statement were less willing to get vaccinated. Furthermore, this led to decreased trust in the health authority and encouraged the endorsement of science denialist thinking. The societal consequences of such communication are therefore manifold; the researchers call for new guidelines to ensure careful and evidence-based communication by health authorities. The findings have recently been published in the renowned journal Science.

In the new paper, “Toward a Science of Human–AI Teaming for Decision Making: A Complementarity Framework,” researchers present a framework for understanding and designing human–AI teams for decision making. Drawing on collective intelligence research, the framework focuses on reasoning, memory, and attention as core processes that can be distributed across people and AI systems. Published in PNAS Nexus, the work offers guidance for researchers, practitioners, and policymakers seeking to build human–AI teams that are effective, accountable, and aligned with human values.