Big Data and Regulation (selected literature)
Coglianese C. (2020)
Deploying Machine Learning for a Sustainable Future
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.
Regulation and Covid-19
Zhoudan Xie (2020)
Regulation during COVID-19 News Sentiment Improved, While Uncertainty Remains
Scholars have identified various regulatory barriers hampering responses to the COVID-19 pandemic. For example, the regulatory approval required for drugs and medical devices has created “bottlenecks” for expanding the capacity of virus testing, ambiguous and often changing regulations “have served as hindrances” to the increasing use of telehealth, and patients have limited access to mobile narcotic treatment due to regulatory bans. Do these criticisms reflect the public’s opinion toward regulation, and how did average public sentiment evolve with the spread of COVID-19? This article explores these questions by presenting a text-based sentiment analysis of news articles related to COVID-19 and regulation. The analysis shows that the expression about regulation in the COVID-related news was negative in most days during the beginning of the virus outbreak, but it started to improve in mid-March. The improvement may suggest increased public confidence in regulatory responses to the pandemic, as the government started to take the virus more seriously and regulatory agencies started to issue temporary relaxations of regulations. However, the level of uncertainty expressed in the news shows no signs of diminishing, indicating persistent uncertainty surrounding regulation in the time of COVID-19. Further topic modeling of news articles suggests that sentiment and uncertainty vary across different regulatory issues. News covering quarantine and reopening, legislation (other than the stimulus bill), and testing and treatment revealed the most negative sentiment, and uncertainty was relatively high regarding testing and treatment, workplace safety, banking and lending, and oil prices.
Big Data and Regulation (selected literature)
Freeman Engsrom D. e Ho D. E. (2020)
Algorithmic Accountability in the Administrative State
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
Big Data and Regulation (selected literature)
Freeman Engsrom D. et al. (2020)
Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies
Artificial intelligence (AI) promises to transform how government agencies do their work. Rapid developments in AI have the potential to reduce the cost of core governance functions, improve the quality of decisions, and unleash the power of administrative data, thereby making government performance more efficient and effective. Agencies that use AI to realize these gains will also confront important questions about the proper design of algorithms and user interfaces, the respective scope of human and machine decision-making, the boundaries between public actions and private contracting, their own capacity to learn over time using AI, and whether the use of AI is even permitted. These are important issues for public debate and academic inquiry. Yet little is known about how agencies are currently using AI systems beyond a few headline-grabbing examples or surface-level descriptions. Moreover, even amidst growing public and scholarly discussion about how society might regulate government use of AI, little attention has been devoted to how agencies acquire such tools in the first place or oversee their use. In an effort to fill these gaps, the Administrative Conference of the United States (ACUS) commissioned this report from researchers at Stanford University and New York University. The research team included a diverse set of lawyers, law students, computer scientists, and social scientists with the capacity to analyze these cutting-edge issues from technical, legal, and policy angles. The resulting report offers three cuts at federal agency use of AI: (i) a rigorous canvass of AI use at the 142 most significant federal departments, agencies, and sub-agencies (Part I) (ii) a series of in-depth but accessible case studies of specific AI applications at eight leading agencies (SEC, CPB, SSA, USPTO, FDA, FCC, CFPB, USPS) covering a range of governance tasks (Part II); and (iii) a set of cross-cutting analyses of the institutional, legal, and policy challenges raised by agency use of AI (Part III).
International Organisations and Networks: selected documents
OECD (2020)
Innovative Citizen Participation and New Democratic Institutions. Catching the Deliberative Wave
Public authorities from all levels of government increasingly turn to Citizens' Assemblies, Juries, Panels and other representative deliberative processes to tackle complex policy problems ranging from climate change to infrastructure investment decisions. They convene groups of people representing a wide cross-section of society for at least one full day – and often much longer – to learn, deliberate, and develop collective recommendations that consider the complexities and compromises required for solving multifaceted public issues. This "deliberative wave" has been building since the 1980s, gaining momentum since around 2010. This report has gathered close to 300 representative deliberative practices to explore trends in such processes, identify different models, and analyse the trade-offs among different design choices as well as the benefits and limits of public deliberation. It includes Good Practice Principles for Deliberative Processes for Public Decision Making, based on comparative empirical evidence gathered by the OECD and in collaboration with leading practitioners from government, civil society, and academics. Finally, the report explores the reasons and routes for embedding deliberative activities into public institutions to give citizens a more permanent and meaningful role in shaping the policies affecting their lives.
Good Regulation and Public Policies Evaluation: selected literature
Listorti and others (2020)
Towards an Evidence‐Based and Integrated Policy Cycle in the EU: A Review of the Debate on the Better Regulation Agenda
Our aim is to provide an overview of the debate on the Better Regulation (BR) Agenda presentedin 2015 by the European Commission. In addition to academic literature, we also consider studiesand reports from Think Tanks, international organizations, and EU internal scrutinizing bodies.After presenting the main aspects of the debate on some overarching elements of the BR Agenda,we focus in particular on two highlights: evidence-based policymaking and the integrated policycycle. We structure the discussion around main achievements, remaining critical aspects and sug-gestions about what could be further improved. Findings show that although the Commission re-mains a front-runner when it comes to Better Regulation, more efforts are needed to go the lastmile when it comes to evidence-based policymaking and closing the policy cycle. We find thatthe great majority of arrticles welcome the ambition of th e BR Agenda reform. At the same time,they also make clear that to effectively improve regulation, further methodological guidance andconcrete efforts in implementation are needed. Another interesting finding is that the debate onthe BR Agenda is extremely varied, covering normative as well as very technical aspects. It ap-pears, though, to be confined within certain academic fields (namely political science, public ad-ministration, and law), while other fields that can be, in practice, also deeply linked to BRrelated activities are less represented