The Misleading count: an identity-based intervention to counter partisan misinformation sharing

The impact of generative artificial intelligence on socioeconomic inequalities and policy making

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

Social norm change: drivers and consequences

Social norms research is booming. In recent years, several experts have recommended using social norms (unwritten rules that prescribe what people ought or ought not to do) to confront the societal, environmental and health challenges our societies face. If we are to do so, a better understanding is required of how social norms themselves emerge, evolve and respond to these challenges. Social norms have long been used as post hoc explanations of behaviour or are seen as stable social constructs. Yet norms evolve dynamically with the changing group processes (e.g. political polarization, kinship structures) and societal challenges (e.g. pandemics, climate change) for which they are presented as solutions. The Theme Issue 'Social norm change: drivers and consequences' contains 14 contributions that present state-of-the-art approaches to understand what generates social norm change and how this impacts our societies. Contributions give insight into (i) the identification of norms, norm change and their effect on behaviour; (ii) drivers and consequences of spontaneous norm change; and (iii) how norm change can be engineered to promote desired behavioural change. This article is part of the theme issue 'Social norm change: drivers and consequences'.