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This volume provides an analytical framework and operational approaches needed for the implementation of results-based accountability. The volume makes a major contribution to the literature on public management and evaluation. Major subject areas covered in this book include: performance based accountability, e-government, legal and institutional framework to hold government to account; fighting corruption; external accountability and the role of supreme audit institutions on detecting fraud and corruption.

Public Expenditure Tracking Surveys (PETS) are a tried and tested methodology to identify delays in financial and in-kind transfers, leakages, and other inefficiencies in government programs. This guidebook aims to provide a starting point for civil society groups and other organizations interested in taking a closer look at government spending processes, both on a small and a larger scale. It is designed to lead users from the definition of the appropriate Public Expenditure Tracking Survey to be used, to the dissemination of its findings, with an emphasis on using evidence effectively to influence policy decisions at any level. Based on the experience of the World Bank in measuring and improving the effectiveness of service delivery, this approach helps empower citizens to keep service providers accountable through better information, communication, and engagement.

Transparent and prudent local financial management has come to be recognized as critical to the integrity of local public sector and to gaining and retaining trust of local residents. Such integrity and trust is sometimes lacking in some local governments in developing countries, especially in the Africa region. This volume attempts to provide practical guidance to local governments interested in establishing sound financial management systems. Leading international experts have contributed to all relevant aspects of local public financial management - cash management, internal controls, accounts, audits, and debt management.

The Individuals with Disabilities Education Act has undergone revisions roughly every 5 years since inception. Despite these modifications, the academic and social outcomes for students with disabilities lag behind those of their regular education peers. Results-Driven Accountability (RDA) was initiated to improve special education outcomes and efficacy. This case study examined the implementation science framework used for RDA. The results surfaced successes and concerns with the implementation process and illuminated barriers unrelated to the RDA initiative but critical for implementation and scaling. The findings from this study may contribute to identifying best practices in large-scale systemic initiatives.

The Clinical and Translational Science Award (CTSA) Consortium and the National Center for Advancing Translational Science (NCATS) undertook a Common Metrics Initiative to improve research processes across the national CTSA Consortium. This was implemented by Tufts Clinical and Translational Science Institute at the 64 CTSA academic medical centers. Three metrics were collaboratively developed by NCATS staff, CTSA Consortium teams, and outside consultants for Institutional Review Board Review Duration, Careers in Clinical and Translational Research, and Pilot Award Publications and Subsequent Funding. The implementation program included training on the metric operational guidelines, data collection, data reporting system, and performance improvement framework. The implementation team provided small-group coaching and technical assistance. Collaborative learning sessions, driver diagrams, and change packages were used to disseminate best and promising practices. After 14 weeks, 84% of hubs had produced a value for one metric and about half had produced an initial improvement plan. Overall, hubs reported that the implementation activities facilitated their Common Metrics performance improvement process. Experiences implementing the first three metrics can inform future directions of the Common Metrics Initiative and other research groups implementing standardized metrics and performance improvement processes, potentially including other National Institutes of Health institutes and centers.

Results-based school accountability has been the linchpin of education reform in the United States for at least two decades. But the COVID-19 pandemic led many schools, states, and districts to put their testing and accountability programs on hold. Chester E. Finn, Jr., argues that it is time for the accountability holiday to end and makes recommendations for how to ensure the accountability programs are effective. Requirements for effective accountability include rigorous standards; assessments and other metrics; consequences for failing to meet, meeting, and exceeding standards; and the capacity to make changes needed to improve.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

Significant theories of democratic accountability hinge on how political campaigns affect Americans’ candidate choices. We argue that the best estimate of the effects of campaign contact and advertising on Americans’ candidates choices in general elections is zero. First, a systematic meta-analysis of 40 field experiments estimates an average effect of zero in general elections. Second, we present nine original field experiments that increase the statistical evidence in the literature about the persuasive effects of personal contact tenfold. These experiments’ average effect is also zero. In both existing and our original experiments, persuasive effects only appear to emerge in two rare circumstances. First, when candidates take unusually unpopular positions and campaigns invest unusually heavily in identifying persuadable voters. Second, when campaigns contact voters long before election day and measure effects immediately—although this early persuasion decays. These findings contribute to ongoing debates about how political elites influence citizens’ judgments.