Narratives and Expert Information in Agenda-Setting: Experimental Evidence on State Legislator Engagement with Artificial Intelligence Policy

Are narratives as influential in gaining the attention of policymakers as expert information, including for complex, technical policy domains such as artificial intelligence (AI) policy? This pre-registered study uses a field experiment to evaluate legislator responsiveness to policy entrepreneur outreach. In partnership with a leading AI think tank, we send more than 7300 U.S. state legislative offices emails about AI policy containing an influence strategy (providing a narrative, expert information, or the organization\u27s background), along with a prominent issue frame about AI (emphasizing technological competition or ethical implications). To assess engagement, we measure link clicks to further resources and webinar registration and attendance. Although AI policy is a highly technical domain, we find that narratives are just as effective as expert information in engaging legislators. Compared to control, expert information and narratives led to 28 and 34 percent increases in policymaker engagement, respectively. Furthermore, higher legislature professionalism and lower state-level prior AI experience are associated with greater engagement with both narratives and expert information. Finally, we find that policymakers are equally engaged by an ethical framing of AI policy as they are with an economic one. The findings advance efforts to bridge scholarship on policy narratives, policy entrepreneurship, and agenda-setting

Setting the Agenda for AI: Actors, Issues, and Influence in United States Artificial Intelligence Policy

As research and adoption of artificial intelligence (AI) has significantly advanced in the early 21st century, determining how to govern AI has become a global priority. Key questions include how AI should be understood as a policy domain, which policy problems are most pressing, which solutions are most viable, and who should have a say in this process. This dissertation seeks to provide key insights into the early years of AI policy, focusing on the development of the emerging AI policy agenda in the United States. To do so, it examines and reveals which issues, actors, and influence efforts are playing a prominent role in the complex, ambiguous, and contested process of agenda-setting. The research performed draws on a variety of quantitative and qualitative methodologies, including document analysis, text-as-data and time series approaches, and experimental techniques. Data examined include text from U.S. federal AI policy documents, traditional and social media discourse from federal policymakers, media, and members of the public, and engagement data collected from state legislators who participated in a field experiment. The results reveal that social and ethical dimensions of AI receive a heightened degree of attention in AI policy discourse. However, consideration of these issues remains partially superficial and subsumed into concern about AI's potential for economic innovation and role in geopolitical competition. Further findings demonstrate that policy entrepreneurs can use persuasive narratives to influence legislators about AI policy, and that these narratives are just as effective as technical information. Finally, despite pervasive calls for public participation in AI governance, the public does not appear to play a key role in directing attention to AI's social and ethical implications nor in shaping concrete policy solutions, such that the emerging AI agenda remains primarily expert-driven. The dissertation's findings and theoretical and methodological approaches offer key contributions to policy process scholarship and related fields of research, and provide a baseline on which to understand the evolution of the AI policy agenda and AI governance going forward.Ph.D

Professional Reading: The Lebanon War

Israel\u27s Lebanon Wa

The Impact of Community Engagement on Undergraduate Social Responsibility Attitudes

The literature on student development cautions that social responsibility attitudes may stagnate or decline as students proceed through college. Given the importance of students’ future professional obligations to society, identifying ways to reverse this trend is crucial. In turn, an important aim of this study, situated at a large public university, is to evaluate the prospects of community engagement as a strategy to foster professional social responsibility development. The study uses longitudinal results from an instrument known as the Generalized Professional Responsibility Assessment (GPRA) to assess personal and professional social responsibility attitudes. The study’s sample includes 128 students who completed a survey both in 2017, when entering college, and in 2019, when near the midpoint of college. Findings indicate that social responsibility attitudes remain stagnant, and that students over that time period attach more importance to salary as compared to helping people when considering job priorities. Yet, results reveal that increased community engagement predicts growth in social responsibility attitudes, even when controlling for students’ pre-college social responsibility attitudes and demographic characteristics. Further, a novel contribution of this study is a focus on two sub-categories of community engagement: discipline-based and peer-based. Discipline-based community engagement appears to foster professional aspects of social responsibility, while community engagement experiences tied to peer interaction appear to exert greater impacts for non-White students. An observation derived from the study is that community engagement, particularly when it connects to a student’s discipline or draws on peer influences, could be an effective strategy to promote social responsibility development

What Governs Attitudes Toward Artifcial Intelligence Adoption and Governance?

Designing effective and inclusive governance and public communication strategies for artificial intelligence (AI) requires understanding how stakeholders reason about its use and governance. We examine underlying factors and mechanisms that drive attitudes toward the use and governance of AI across six policy-relevant applications using structural equation modeling and surveys of both US adults (N = 3,524) and tech- nology workers enrolled in an online computer science master’s degree program (N = 425). We find that the cultural values of individualism, egalitarianism, general risk aversion, and techno-skepticism are important drivers of AI attitudes. Perceived benefit drives attitudes toward AI use but not its governance. Experts hold more nuanced views than the public and are more supportive of AI use but not its regulation. Drawing on these findings, we discuss challenges and opportunities for participatory AI governance, and we recommend that trustworthy AI governance be emphasized as strongly as trustworthy AI

Excitability in autonomous Boolean networks

We demonstrate theoretically and experimentally that excitable systems can be built with autonomous Boolean networks. Their experimental implementation is realized with asynchronous logic gates on a reconfigurabe chip. When these excitable systems are assembled into time-delay networks, their dynamics display nanosecond time-scale spike synchronization patterns that are controllable in period and phase.Comment: 6 pages, 5 figures, accepted in Europhysics Letters (epljournal.edpsciences.org

Description of the Efficacy and Safety of Three New Biologics in the Treatment of Rheumatoid Arthritis

English articles on abatacept, golimumab, and tocilizumab in rheumatoid arthritis published between 2002 and 2009 were reviewed systematically. All randomized clinical trials, open-label extensions, meta-analyses, and reviews were examined. There were thirteen articles on abatacept, four on golimumab, and seven on tocilizumab. All three drugs were effective in methotrexate-naïve, methotrexate-incomplete responders, and tumor-necrosis-factor-failure rheumatoid arthritis patients. Of the three, only abatacept has been tested in a head-to-head trial with infliximab, in which it was found to be equivalent to infliximab. Golimumab resulted in a more modest improvement than the others in methotrexate-naïve patients, although no direct comparisons among the three drugs were possible or appropriate. Descriptive analysis of adverse events showed that patients receiving abatacept, golimumab, and tocilizumab were subject to more adverse events than controls overall, as expected. In the abatacept studies, a few cases of tuberculosis, more cardiovascular events and gastrointestinal bleedings and more basal cell carcinoma were seen. Golimumab was associated with more skin rashes and pneumonia, while tocilizumab was associated with increased lipids, more liver-function abnormalities, and neutropenia. These new medications are useful additions to the rheumatologic armamentarium and represent greater convenience (golimumab) or different mechanisms of action (abatacept and tocilizumab) than tumor-necrosis-factor inhibitors for treating rheumatoid arthritis. As expected, some adverse events occur when using these drugs and patients need to be watched carefully

A Prescription for Improving Drug Formulary Decision Making

Gordon Schiff and colleagues present a new tool and checklist to help formularies make decisions about drug inclusion and to guide rational drug use