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Looking for ways to handle the transition to a digital economy Robots, artificial intelligence, and driverless cars are no longer things of the distant future. They are with us today and will become increasingly common in coming years, along with virtual reality and digital personal assistants. As these tools advance deeper into everyday use, they raise the question-how will they transform society, the economy, and politics? If companies need fewer workers due to automation and robotics, what happens to those who once held those jobs and don't have the skills for new jobs? And since many social benefits are delivered through jobs, how are people outside the workforce for a lengthy period of time going to earn a living and get health care and social benefits? Looking past today's headlines, political scientist and cultural observer Darrell M. West argues that society needs to rethink the concept of jobs, reconfigure the social contract, move toward a system of lifetime learning, and develop a new kind of politics that can deal with economic dislocations. With the U.S. governance system in shambles because of political polarization and hyper-partisanship, dealing creatively with the transition to a fully digital economy will vex political leaders and complicate the adoption of remedies that could ease the transition pain. It is imperative that we make major adjustments in how we think about work and the social contract in order to prevent society from spiraling out of control. This book presents a number of proposals to help people deal with the transition from an industrial to a digital economy. We must broaden the concept of employment to include volunteering and parenting and pay greater attention to the opportunities for leisure time. New forms of identity will be possible when the \"job\" no longer defines people's sense of personal meaning, and they engage in a broader range of activities. Workers will need help throughout their lifetimes to acquire new skills and develop new job capabilities. Political reforms will be necessary to reduce polarization and restore civility so there can be open and healthy debate about where responsibility lies for economic well-being. This book is an important contribution to a discussion about tomorrow-one that needs to take place today.

In the decades following World War II, American scientists were celebrated for their contributions to social and technological progress. They were also widely criticized for their increasingly close ties to military and governmental power--not only by outside activists but from among the ranks of scientists themselves. Disrupting Science tells the story of how scientists formed new protest organizations that democratized science and made its pursuit more transparent. The book explores how scientists weakened their own authority even as they invented new forms of political action.

An informed and practical road map for controlling disinformation, embracing free speech, saving American elections, and protecting democracy \"A fresh, persuasive and deeply disturbing overview of the baleful and dangerous impact on the nation of widely disseminated false speech on social media. Richard Hasen, the country's leading expert about election law, has written this book with flair and clarity.\"-Floyd Abrams, author of The Soul of the First Amendment What can be done consistent with the First Amendment to ensure that American voters can make informed election decisions and hold free elections amid a flood of virally spread disinformation and the collapse of local news reporting? How should American society counter the actions of people like former President Donald J. Trump, who used social media to convince millions of his followers to doubt the integrity of U.S. elections and helped foment a violent insurrection? What can we do to minimize disinformation campaigns aimed at suppressing voter turnout? With piercing insight into the current debates over free speech, censorship, and Big Tech's responsibilities, Richard L. Hasen proposes legal and social measures to restore Americans' access to reliable information on which democracy depends. In an era when quack COVID treatments and bizarre QAnon theories have entered mainstream, this book explains how to assure both freedom of ideas and a commitment to truth.

The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 million deaths, and 13 billion vaccine doses have been reported. It is crucial to evaluate sociopsychological impacts as well as public health indicators such as these to understand the effects of the COVID-19 pandemic. This study aimed to explore the sentiments of residents of major US cities toward restrictions on social activities in 2022 during the transitional phase of the COVID-19 pandemic, from the peak of the pandemic to its gradual decline. By illuminating people's susceptibility to COVID-19, we provide insights into the general sentiment trends during the recovery phase of the pandemic. To analyze these trends, we collected posts (N=119,437) on the social media platform Twitter (now X) created by people living in New York City, Los Angeles, and Chicago from December 2021 to December 2022, which were impacted by the COVID-19 pandemic in similar ways. A total of 47,111 unique users authored these posts. In addition, for privacy considerations, any identifiable information, such as author IDs and usernames, was excluded, retaining only the text for analysis. Then, we developed a sentiment estimation model by fine-tuning a large language model on the collected data and used it to analyze how citizens' sentiments evolved throughout the pandemic. In the evaluation of models, GPT-3.5 Turbo with fine-tuning outperformed GPT-3.5 Turbo without fine-tuning and Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa)-large with fine-tuning, demonstrating significant accuracy (0.80), recall (0.79), precision (0.79), and F -score (0.79). The findings using GPT-3.5 Turbo with fine-tuning reveal a significant relationship between sentiment levels and actual cases in all 3 cities. Specifically, the correlation coefficient for New York City is 0.89 (95% CI 0.81-0.93), for Los Angeles is 0.39 (95% CI 0.14-0.60), and for Chicago is 0.65 (95% CI 0.47-0.78). Furthermore, feature words analysis showed that COVID-19-related keywords were replaced with non-COVID-19-related keywords in New York City and Los Angeles from January 2022 onward and Chicago from March 2022 onward. The results show a gradual decline in sentiment and interest in restrictions across all 3 cities as the pandemic approached its conclusion. These results are also ensured by a sentiment estimation model fine-tuned on actual Twitter posts. This study represents the first attempt from a macro perspective to depict sentiment using a classification model created with actual data from the period when COVID-19 was prevalent. This approach can be applied to the spread of other infectious diseases by adjusting search keywords for observational data.

Background The increasing integration of health information technology (health IT) into the U.S. healthcare system has brought both opportunities for improvement and new vulnerabilities. The 2024–2030 Federal Health IT Strategic Plan emphasizes equitable data access, quality representative data, and the responsible use of artificial intelligence (AI) to improve health outcomes. Yet, the growing complexity of digital infrastructures has amplified risks related to privacy and the security of protected health information (PHI). This study examines U.S. health system vulnerabilities by analyzing reported PHI breaches and situating them within evolving federal health IT priorities. Methods This mixed-methods descriptive study combines quantitative analysis of the U.S. Department of Health and Human ServiCE’s (HHS) Breach Portal data (2013–2023) with a qualitative review of federal policy and regulatory developments related to health IT. Breaches of PHI affecting more than 500 individuals were included, consistent with HHS reporting requirements. Duplicate and incomplete entries were removed. Breaches were categorized by cause and type. Quantitative results describe frequencies, proportions, and trends, while qualitative analysis of policy documents and breach narratives contextualizes these findings within the broader framework of digital health governance. Results From 2013 to 2023, the total number of reported PHI breaches and the share attributed to hacking and IT incidents increased markedly, while those involving theft, loss, or improper disposal declined. Healthcare providers accounted for most reported breaches, followed by business associates and health plans. Despite advances in interoperability and automation, the healthcare sector remains disproportionately affected by cybersecurity incidents. The qualitative analysis reveals persistent gaps between federal strategic goals and the practical implementation of privacy and security safeguards across healthcare. Conclusion This study underscores the paradox of digital transformation: while health IT adoption improves efficiency, coordination, and data sharing, it simultaneously exposes the healthcare system to new risks. Strengthening system resilience requires harmonized governance, continuous monitoring, and greater investment in digital literacy. As AI use and automation expand, policy reforms must ensure that innovation does not compromise patient privacy or deepen inequities. These findings contribute to a better understanding of health system vulnerabilities and offer insights for enhancing the security and resilience of the U.S. health system.

The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Our consensus process identified key contextual factors of trust, namely, an AI system's environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.

Apple's high-profile 2016 fight with the FBI, in which the company challenged a court order commanding it to help unlock the iPhone of one of the San Bernardino terrorists, exemplifies how central the question of regulating government surveillance has become in U.S. politics and law. But scholarly attempts to answer this question have suffered from a serious omission. Scholars have ignored how government surveillance is checked by surveillance intermediaries: companies like Apple, Google, and Facebook that dominate digital communications and data storage and on whose cooperation government surveillance relies. This Article fills this gap in the scholarly literature, providing the first comprehensive analysis of how surveillance intermediaries constrain the surveillance executive, the law enforcement and foreign-intelligence agencies that conduct surveillance. In so doing, it enhances our conceptual understanding of, and thus our ability to improve, the institutional design of government surveillance. Surveillance intermediaries have financial and ideological incentives to resist government requests for user data. Their techniques of resistance are proceduralism and litigiousness that reject voluntary cooperation in favor of minimal compliance and aggressive litigation; technological unilateralism, in which companies design products and services to make surveillance harder; and policy mobilization that rallies legislative and public opinion against government surveillance. Surveillance intermediaries also enhance the surveillance separation of powers: They make the surveillance executive more subject to interbranch constraints from Congress and the courts and to intrabranch constraints from economic and foreign relations agencies as well as from the surveillance executive s own surveillance-limiting components. The normative implications of this descriptive account are important and crosscutting. Surveillance intermediaries can both improve and worsen the surveillancefrontier, the set of tradeoffs between public safety, privacy, and economic growth from which we choose surveillance policy. They enhance surveillance self government—the democratic supervision over surveillance policy—when they mobilize public opinion and strengthen the surveillance separation of powers. But they undermine it when their unilateral technological changes prevent the government from exercising its lawful surveillance authorities.

In the digital age, there is an emerging area of research focusing on digital well-being (DWB), yet conceptual frameworks of this novel construct are lacking. The current conceptualization either approaches the concept as the absence of digital ill-being, running the risk of pathologizing individual digital use, or follows the general subjective well-being framework, failing to highlight the complex digital nature at play. This preregistered study aimed to address this gap by using a network analysis, which examined the strength of the relationships among affective (digital stress and web-based hedonic well-being), cognitive (online intrinsic needs satisfaction), and social (online social connectedness and state empathy) dimensions of DWB and their associations with some major DWB protective and risk factors (ie, emotional regulation, nomophobia, digital literacy, self-control, problematic internet use, coping styles, and online risk exposure). The participants were 578 adults (mean age 38.7, SD 13.14 y; 277/578, 47.9% women) recruited from the United Kingdom and the United States who completed an online survey. Two network models were estimated. The first one assessed the relationships among multiple dimensions of DWB, and the second examined the relationships between DWB dimensions and related protective and risk factors. The 2 resulting network structures demonstrated high stability, with the correlation stability coefficients being 0.67 for the first and 0.75 for the second regularized Gaussian graphical network models. The first network indicated that all DWB variables were positively related, except for digital stress, which was negatively correlated with the most central node-online intrinsic needs satisfaction. The second network revealed 2 distinct communities: digital competency and digital dependency. Emotional regulation emerged as the most central node with the highest bridge expected influence, positively associated with emotion-focused coping in the digital competency cluster and negatively associated with avoidant coping in the digital dependency cluster. In addition, some demographic differences were observed. Women scored higher on nomophobia (χ =10.7; P=.03) and emotion-focused coping (χ =14.9; P=.01), while men scored higher on digital literacy (χ =15.2; P=.01). Compared with their older counterparts, younger individuals scored lower on both emotional regulation (Spearman ρ=0.27; P<.001) and digital self-control (Spearman ρ=0.35; P<.001) and higher on both digital stress (Spearman ρ=-0.14; P<.001) and problematic internet use (Spearman ρ=-0.25; P<.001). The network analysis revealed how different aspects of DWB were interconnected, with the cognitive component being the most influential. Emotional regulation and adaptive coping strategies were pivotal in distinguishing digital competency from dependency.

OpenAI's ChatGPT is a source of advanced online health information (OHI) that may be integrated into individuals' health information-seeking routines. However, concerns have been raised about its factual accuracy and impact on health outcomes. To forecast implications for medical practice and public health, more information is needed on who uses the tool, how often, and for what. This study aims to characterize the reasons for and types of ChatGPT OHI use and describe the users most likely to engage with the platform. In this cross-sectional survey, patients received invitations to participate via the ResearchMatch platform, a nonprofit affiliate of the National Institutes of Health. A web-based survey measured demographic characteristics, use of ChatGPT and other sources of OHI, experience characterization, and resultant health behaviors. Descriptive statistics were used to summarize the data. Both 2-tailed t tests and Pearson chi-square tests were used to compare users of ChatGPT OHI to nonusers. Of 2406 respondents, 21.5% (n=517) respondents reported using ChatGPT for OHI. ChatGPT users were younger than nonusers (32.8 vs 39.1 years, P<.001) with lower advanced degree attainment (BA or higher; 49.9% vs 67%, P<.001) and greater use of transient health care (ED and urgent care; P<.001). ChatGPT users were more avid consumers of general non-ChatGPT OHI (percentage of weekly or greater OHI seeking frequency in past 6 months, 28.2% vs 22.8%, P<.001). Around 39.3% (n=206) respondents endorsed using the platform for OHI 2-3 times weekly or more, and most sought the tool to determine if a consultation was required (47.4%, n=245) or to explore alternative treatment (46.2%, n=239). Use characterization was favorable as many believed ChatGPT to be just as or more useful than other OHIs (87.7%, n=429) and their doctor (81%, n=407). About one-third of respondents requested a referral (35.6%, n=184) or changed medications (31%, n=160) based on the information received from ChatGPT. As many users reported skepticism regarding the ChatGPT output (67.9%, n=336), most turned to their physicians (67.5%, n=349). This study underscores the significant role of AI-generated OHI in shaping health-seeking behaviors and the potential evolution of patient-provider interactions. Given the proclivity of these users to enact health behavior changes based on AI-generated content, there is an opportunity for physicians to guide ChatGPT OHI users on an informed and examined use of the technology.