Publications

The European economy is now recovering briskly, after an unprecedented fall in output during the second quarter of 2020. But this recovery is likely to be incomplete for some time, not least because of the substantial degree of social distancing measures still in place. The defining feature of the present situation is that the remaining demand and supply obstacles are highly sector specific. Aggregate demand management will thus be less effective. Income replacement measures, such as short-term work schemes, will be needed for some time, but should be applied flexibly to support rather than hinder structural adjustment. This also applies to the funds to be made available under the €750 bn Recovery and Resilience Facility. Money is fungible. This means that the key for success will not be the projects to be financed by the RRF, but whether member states undertake structural reforms that increase their growth potential.

To meet the environmental challenges of a warming planet and an increasingly complex, high techeconomy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that environmental agencies will need to surmount if they are to take full advantage of big data and predictive analytics. First, agencies must invest in upgrading their information technology infrastructure to take advantage of computational advances. Relatively modest technology investments, if made wisely, could support the use of algorithmic tools that could yield substantial savings in other administrative costs. Second, agencies will need to confront emerging concerns about privacy, fairness, and transparency associated with its reliance on Big Data and algorithmic analyses. Third, government agencies will need to strengthen their human capital so that they have the personnel who understand how to use machine learning responsibly. Finally, to work well, algorithms will need clearly defined objectives. Environmental officials will need to continue to engage with elected officials, members of the public, environmental groups, and industry representatives to forge clarity and consistency over how various risk and regulatory objectives should be specified in machine learning tools. Overall, with thoughtful planning, adequate resources, and responsible management, governments should be able to overcome the obstacles that stand in the way of the use of artificial intelligence to improve environmental sustainability. If policy makers and the public will recognize the need for smarter governance, they can then start to tackle obstacles that stand in its way and better position society for a more sustainable future.

Policy evaluation is a critical element of good governance, as it promotes public accountability and contributes to citizens’ trust in government. Evaluation helps ensure that decisions are rooted in trustworthy evidence and deliver desired outcomes. Drawing on the first significant cross-country survey of policy evaluation practices covering 42 countries, this report offers a systemic analysis of the institutionalisation, quality and use of evaluation across countries and looks at how these three dimensions interrelate. The report also covers cross-cutting aspects related to regulatory assessment and performance budgeting. The analysis illustrates the role and functions of key institutions within the executive, such as centres of government and ministries of finance. It also underlines the role of supreme audit institutions.

How will artificial intelligence (AI) transform government? Stemming from a major study commissioned by the Administrative Conference of the United States (ACUS), we highlight the promise and trajectory of algorithmic tools used by federal agencies to perform the work of governance. Moving past the abstract mappings of transparency measures and regulatory mechanisms that pervade the current algorithmic accountability literature, our analysis centers around a detailed technical account of a pair of current applications that exemplify AI’s move to the center of the redistributive and coercive power of the state: the Social Security Administration’s use of AI tools to adjudicate disability benefits cases and the Securities and Exchange Commission’s use of AI tools to target enforcement efforts under federal securities law. We argue that the next generation of work will need to push past a narrow focus on constitutional law and instead engage with the broader terrain of administrative law, which is far more likely to modulate use of algorithmic governance tools going forward. We demonstrate the shortcomings of conventional ex ante and ex post review under current administrative law doctrines and then consider how administrative law might adapt in response. Finally, we ask how to build a sensible accountability structure around public sector use of algorithmic governance tools while maintaining incentives and opportunities for salutary innovation. Reviewing and rejecting commonly offered solutions, we propose a novel approach to oversight centered on prospective benchmarking. By requiring agencies to reserve a random set of cases for manual decision making, benchmarking offers a concrete and accessible test of the validity and legality of machine outputs, enabling agencies