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Government can sabotage itself. From the President's choice of agency heads to agency budgets, regulations, and litigating positions, presidents and their appointees have undermined the very programs they administer. But why would an agency try to put itself out of business? And how can agencies that are subject to an array of political and legal checks sabotage statutory programs? This article offers an account of the \"what, why, and how\" of administrative sabotage that answers those questions. It contends that sabotage reflects a distinct mode of agency action that is more permanent, more destructive, and more democratically illegitimate than more-studied forms of maladministration. In contrast to an agency that shirks its statutory duties or drifts away from Congress's policy goals, one engaged in sabotage aims deliberately to kill or nullify a program it administers. Agencies sabotage because presidents ask them to. Facing pressure to dismantle statutory programs in an environment where securing legislation from Congress is difficult and politically costly, Presidents pursue retrenchment through the administrative state. Building on this positive theory of administrative sabotage, this article considers legal responses. The best response, this article contends, is not reforms to the cross-cutting body of administrative law that structures most agency action. Rather, the risk of sabotage is better managed through changes to how statutory programs are designed. Congress's choices about agency leadership, the concentration or dispersal of authority to implement statutory programs, the breadth of statutory delegations, and other matters influence the likelihood that sabotage will succeed or fail. When lawmakers create or modify federal programs, they should design them to be less vulnerable to sabotage by the very agencies that administer them.

The capture theory of regulation concludes that regulatory agencies act in the interests of regulated firms rather than in the general public interest. Those benefits to regulated firms are transitory. In the long run, regulator}' protections benefit legislators and regulators by making those who are regulated dependent on those who have the power to extend or terminate those regulatory protections. Regulated firms must continue to support the interests of the politicians on whose decisions their profitability depends. Ultimately, firms are captured by those who regulate them, rather than the other way around.

Abstract Nursing homes (NHs) are one of the most heavily regulated industries in the United States. However, concerns about quality of care remain, especially for more vulnerable residents, including those with dementia. Concerns only increased during recent natural disasters and the COVID-19 pandemic. Community and industry advocates have suggested diverse reforms that prompted new federal policies. This study poses the following research questions: (1) How do federal policies compare with proposals by community (staff and patient advocates) and industry (NH) advocates to improve quality of care in NHs before and during the pandemic; and (2) How do federal policies address micro, mezzo, and macro level issues? To address these questions, this study employs a multi-level comparison case study design. Primary data includes systematically collected and analyzed federal bills, CMS regulations, and community and industry reports, press releases, and website data from 2018 through 2022. Findings revealed that policy proposals fell into three categories: reframe, reform, and transform. Reframe approaches included minimal transformation at the micro-level. Reform approaches involved more mezzo and macro-level changes. Transform approaches proposed significant structural changes at the macro-level. Community advocates presented far more transformative changes than industry advocates and federal policymakers. Federal policies focused on reframe and reform solutions at the micro level and rarely proposed transforming macro-level structures (e.g., workforce structural inequities). This study has important implications for research, policy, and practice by exposing limitations and strengths of proposed solutions for addressing resident care and concludes with suggestions for better aligning with community advocates.

Companies and governments now use Artificial Intelligence (Al) in a wide range of settings. But using Al leads to well-known risks that arguably present challenges for a traditional liability model. It is thus unsurprising that lawmakers in both the United States and the European Union (EU) have turned to the tools of risk regulation in governing Al systems. This Article describes the growing convergence around risk regulation in Al governance. It then addresses the question: what does it mean to use risk regulation to govern Al systems? The primary contribution of this Article is to offer an analytic framework for understanding the use of risk regulation as Al governance. It aims to surface the shortcomings of risk regulation as a legal approach, and to enable readers to identify which type of risk regulation is at play in a given law. The theoretical contribution of this Article is to encourage researchers to think about what is gained and what is lost by choosing a particular legal tool for constructing the meaning of Al systems in the law. Whatever the value of using risk regulation, constructing Al harms as risks is a choice with consequences. Risk regulation comes with its own policy baggage: a set of tools and troubles that have emerged in other fields. Risk regulation tends to try to fix problems with the technology so it may be used, rather than contemplating that it might sometimes not be appropriate to use it at all. Risk regulation works best on quantifiable problems and struggles with quantify harms. can cloak what are really policy decisions as technical decisions. Risk regulation typically is not structured to make injured people whole. And the version of risk regulation typically deployed to govern Al systems lacks the feedback loops of tort liability. Thus the choice to use risk regulation in the first place channels the law towards a particular approach to Al governance that makes implicit tradeoffs and carries predictable shortcomings. The second, more granular observation this Article makes is that not all risk regulation is the same. That is, once regulators choose to deploy risk regulation, there are still significant variations in what type of risk regulation they might use. Risk regulation is a legal transplant with multiple possible origins. This · Article identifies at least four models for Al risk regulation that meaningfully diverge in how they address accountability.

Licensing requirements for US private security firms and guards differ substantially from state to state. State regulatory institutions for this industry also vary considerably. Some states have specialized regulatory boards with industry personnel (guards, firm owners) and/or public police as board members, while others rely on non-specialized regulators such as Departments of Commerce, State, Professional Regulation, or Consumer Affairs. These cross-state variations in licensing requirements and regulatory institutions provide an opportunity to explore relationships between the two. Private security regulation is of particular interest in this context because previous empirical research implies that allocating more resources to private security reduces crime, and that relatively stringent licensing requirements limit entry, thereby increasing crime. A panel of 1991–2010 state data is employed to see if particular regulatory institutions are associated with particular licensing requirements. Empirical results suggest that requirements for entry into this market tend to be relatively strict when active private security personnel are in control of licensing, and that different patterns of regulation generally apply when police or non-specialized agencies control licensing. Therefore, both public-interest and private-interest explanations for the observed relationships between the structure of regulatory institutions and resulting licensing requirements are discussed.

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

This study examines the impact of audit committee (AC) characteristics on earnings management (EM), of FTSE 350 companies, for the fiscal years 2006 and 2007. Characteristics such as number of members and meetings; independence; directors’ remuneration; outside directorships; various types of financial expertise; AC members’ ownership; regulated sector; and the listing on FTSE 100 are investigated. No significant association was found between absolute discretionary accruals and these characteristics, apart from in the regulated industry. The results for signed discretionary accruals show that some characteristics have an effect on either upward or downward EM. This reveals the importance of distinguishing between AC effectiveness on constraining upward and downward EM. This article explores some qualitative AC characteristics, which have not previously been examined. In addition, it is probably the first to examine the effect of several AC characteristics on EM in the UK context. The findings of this study provide useful insights for regulators in order to improve and reconsider the current regulations on AC mechanisms. The findings of generally ineffectual ACs in the United Kingdom, along with many similar findings from other countries (see Xie et al, 2003 and Baxter and Cotter, 2009 ), raise concerns about the current situation of the regulation and the composition of ACs. External auditors are warned against relying on the form rather than substance of ACs.

Stock options are thought to align the interests of CEOs and shareholders, but scholars have shown that options sometimes lead to outcomes that run counter to what they are meant to achieve. Building on this research, we argue that options promote a lack of caution in CEOs that manifests in a higher incidence of product safety problems. We also posit that this relationship varies across CEOs, and that the effect of options will depend upon CEO characteristics such as tenure and founder status. Analyzing product recall data for a large sample of FDA-regulated companies, we find support for our theory.