Abstract 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. 

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

Generative artificial intelligence, including chatbots like ChatGPT, 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 probable impacts of generative AI on four critical domains: work, education, health, and information. Our goal is to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems. Generative AI in the workplace 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 improves diagnostics and accessibility but could deepen pre-existing inequalities. For information, it democratizes content creation and access but also dramatically expands the production and proliferation of misinformation. Each section covers a specific topic, evaluates existing research, identifies critical gaps, and recommends research directions. 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 contend that these policies should promote shared prosperity through the advancement of generative AI. We suggest several concrete policies to encourage further research and debate. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

pdf/the-impact-of-generative-artificial-intelligence-on-xhi7lmpqyt.pdf

Low uptake of the COVID-19 vaccine in the US has been widely attributed to social media misinformation. To evaluate this claim, we introduce a framework combining lab experiments (total 

Quantifying the impact of misinformation and vaccine-skeptical content on Facebook

The vivid creation of a novel reality is often the goal of most immersive Virtual Reality (VR) experiences. While these perceptual simulations can be used for healing, for example improving mental health (Parsons and Rizzo 2008) or recovering from strokes (Fluet and Deutsch 2013), there is growing concern that the same properties of VR that create realistic and persuasive experiences can be used for manipulation (Slater et al. 2020). As noted in Trauthig and Woolley (see this issue), one of the core threats of VR to trust and safety is the potential for VR to amplify misinformation.

/pdf/misinformation-in-virtual-reality-qm83oijs.pdf

Misinformation in Virtual Reality

Generative artificial intelligence, including chatbots like ChatGPT, 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 probable impacts of generative AI on four critical domains: work, education, health, and information. Our goal is to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems. Generative AI in the workplace 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 improves diagnostics and accessibility but could deepen pre-existing inequalities. For information, it democratizes content creation and access but also dramatically expands the production and proliferation of misinformation. Each section covers a specific topic, evaluates existing research, identifies critical gaps, and recommends research directions. 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 contend that these policies should promote shared prosperity through the advancement of generative AI. We suggest several concrete policies to encourage further research and debate. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. 

pdf/the-impact-of-generative-artificial-intelligence-on-e0dx2ujh67.pdf


 We conduct the first large-scale study of image-based political misinformation on Facebook. We collect 13,723,654 posts from 14,532 pages and 11,454 public groups from August through October 2020, posts that together account for nearly all engagement of U.S. public political content on Facebook. We use perceptual hashing to identify duplicate images and computer vision to identify political figures. Twenty-three percent of sampled political images (N = 1,000) contained misinformation, as did 20% of sampled images (N = 1,000) containing political figures. We find enormous partisan asymmetry in misinformation posts, with right-leaning images 5–8 times more likely to be misleading, but little evidence that misleading images generate higher engagement. Previous scholarship, which mostly cataloged links to noncredible domains, has ignored image posts which account for a higher volume of misinformation. This research shows that new computer-assisted methods can scale to millions of images, and help address perennial and long-unanswered calls for more systematic study of visual political communication.

Visual misinformation on Facebook

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Visual misinformation on Facebook

Mo1276 EHLERS DANLOS SYNDROME: ESOPHAGEAL MANIFESTATIONS AND HIGH RESOLUTION MANOMETRY FINDINGS