Policy studies (Policy Studies Institute)最新文献

The ever-increasing attention to artificial intelligence applications in medicine and healthcare requires us to critically evaluate the effectiveness of the current U.S. regulatory environment in this arena. We outline a series of challenges facing the governance of healthcare AI, including an overreliance on self-regulation when many AI developers lack knowledge of relevant regulation or acknowledge their role in preventing harms, lack of shared responsibility for healthcare AI harms, and the lack of transparency and availability of evidence to assess healthcare AI's safety and effectiveness. We advocate for an ecosystem approach to developing potential solutions to these governance challenges. In particular, we identify actions that civil society organizations, funders, healthcare system purchasers, and payers can take to advance healthcare AI governance. We argue that these actors should make coordinated efforts toward advancing transparency and independent assessment of healthcare AI, and therefore can help fill gaps created by the emphasis on self-regulation.

Sharing large digital-first datasets, including for purposes for which they were not originally intended, is a hallmark of the 'big data revolution'. Through their routine operations, private commercial companies collect massive amounts of diverse data from their customers, some of which may interest those working in the public sector, such as health researchers. Researchers and government agencies worldwide have been increasingly using data from commercial entities (such as Google, Microsoft, Apple, Facebook/Meta, Twitter/X and Amazon, among others) to generate health-related insights. This article explores ethical issues raised by the practice of commercial companies sharing consumer data with third-parties for the purposes of promoting health in the sub-Saharan African (SSA) context. First, as an illustrative example, it examines some of the ways telecommunication (telecom) companies in SSA shared mobility data from cellphone users with public health researchers during the COVID-19 pandemic. Second, it examines a recent debate about the ethical responsibilities of companies that collect, process and share user-generated data, drawing implications for the SSA context. Finally, since this is a relatively understudied subject, we point out some areas where future conceptual and empirical work could contribute to the development of relevant ethics guidance and regulatory governance in SSA.

[This corrects the article DOI: 10.1080/01442872.2019.1577375.].

Tax authorities utilize a wide range of instruments to motivate honest taxpaying ranging from strict audits to fair procedures or personalized support, differing from country to country. However, little is known about how these different instruments and taxpayers' trust influence the generation of interaction climates between tax authorities and taxpayers, motivations to comply, and particularly, tax compliance. The present research examines the extended slippery slope framework (eSSF), which distinguishes tax authorities' instruments into different qualities of power of authority (coercive and legitimate) and trust in authorities (reason-based and implicit), to shed light on the effect of differences between power and trust. We test eSSF assumptions with survey data from taxpayers from three culturally different countries (N = 700) who also vary concerning their perceptions of power, trust, interaction climates, and tax motivations. Results support assumptions of the eSSF. Across all countries, the relation of coercive power and tax compliance was mediated by implicit trust. The connection from legitimate power to tax compliance is partially mediated by reason-based trust. The relationship between implicit trust and tax compliance is mediated by a confidence climate and committed cooperation. Theoretical and practical implications are discussed.