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Much scholarship considers how U.S. federal agencies govern artificial intelligence (AI) through rulemaking and their own internal use policies. But agencies have an overlooked AI governance role: setting discretionary grant policy when directing billions of dollars in federal financial assistance. These dollars enable state and local entities to study, create, and use AI. This funding not only goes to dedicated AI programs, but also to grantees using AI in the course of meeting their routine grant objectives. As discretionary grantmakers, agencies guide and restrict what grant winners do -- a hidden lever for AI governance. Agencies pull this lever by setting program objectives, judging criteria, and restrictions for AI use. Using a novel dataset of over 40,000 non-defense federal grant notices of funding opportunity (NOFOs) posted to the U.S. federal grants website between 2009 and 2024, we analyze how agencies regulate the use of AI by grantees. We select records mentioning AI and review their stated goals and requirements. We find agencies promoting AI in notice narratives, shaping adoption in ways other records of grant policy might fail to capture. Of the grant opportunities

This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data on occupational requirements across the federal government from both centralized and decentralized human resource sources, including from the U.S. Office of Personnel Management (OPM) and various federal agencies. While our preliminary findings suggest some significant shifts in required competencies and potential vulnerability of certain roles to AI-driven changes, we provide nuanced insights that support arguments against abrupt or generic approaches to strategic human capital planning around the development of generative AI. The study aims to inform strategic workforce planning and policy

Leadership in the field of AI is vital for our nation's economy and security. Maintaining this leadership requires investments by the federal government. The federal investment in foundation AI research is essential for U.S. leadership in the field. Providing accessible AI infrastructure will benefit everyone. Now is the time to increase the federal support, which will be complementary to, and help drive, the nation's high-tech industry investments.

We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For example, we estimate employee satisfaction from a survey in which the decision of an employee to participate depends on their satisfaction. We model the time at which a respondent 'arrives' to take the survey, leveraging results from the counting processes literature that has been developed to analyze similar problems with survival data. Our approach is particularly useful for nonresponse bias analysis because it relies on different assumptions than traditional adjustments such as poststratification, which assumes the common cause model, the setting in which external factors explain the characteristic measured by the survey and participation. Our motivation is the Federal Employee Viewpoint Survey, which asks federal employees whether they are satisfied with their work organization. Our model suggests that the sample proportion overestimates the proportion of federal employees that are not satisfied with their work organization even after adjustmen

The Federal Open Market Committee (FOMC) sets the federal funds rate, shaping monetary policy and the broader economy. We introduce \emph{FedSight AI}, a multi-agent framework that uses large language models (LLMs) to simulate FOMC deliberations and predict policy outcomes. Member agents analyze structured indicators and unstructured inputs such as the Beige Book, debate options, and vote, replicating committee reasoning. A Chain-of-Draft (CoD) extension further improves efficiency and accuracy by enforcing concise multistage reasoning. Evaluated at 2023-2024 meetings, FedSight CoD achieved accuracy of 93.75\% and stability of 93.33\%, outperforming baselines including MiniFed and Ordinal Random Forest (RF), while offering transparent reasoning aligned with real FOMC communications.

In this study, we examine the Federal Reserve's communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, characterized by notable contextual uncertainty. Through comparative analysis, we juxtapose the Fed's communication during the COVID-19 crisis with its responses during the dot-com and global financial crises, examining content, sentiment, and timing dimensions. Our findings reveal that Fed communication and policy actions were more reactive to the COVID-19 crisis than to previous crises. Additionally, declining sentiment related to financial stability in interest rate announcements and minutes anticipated subsequent accommodative monetary policy decisions. We further document that communicating about UMP has become the "new normal" for the Fed's Federal Open Market Committee meeting minutes

Abstract This book is about the complex and changing relationship between levels of governance in the US and the European Union. On the basis of a transatlantic dialogue between scholars concerned about modes of governance on both sides, it is a collective attempt at analysing the ramifications of the legitimacy crisis in these multi‐layered democracies, and possible remedies to this. Starting from a focus on the current policy debates over ‘devolution’ and ‘subsidiarity’, the book engages the reader into the broader tension of comparative federalism. Its authors believe that in spite of the fundamental differences between them, both the EU and the USA are in the process of re‐defining a federal vision for the twenty‐first century. The book is a contribution to the study of federalism and European integration, and seeks to bridge the divide between the two. It also bridges the traditional divide between technical, legal or regulatory discussions of federal governance and philosophical debates over questions of belonging and multiple identities. It is a multi‐disciplinary project, bringing together historians, political scientists and theorists, legal scholars, sociologists and political economists (more than 20 authors are involved), and includes both innovative analysis and prescriptions on how to reshape the federal contract in the USA and the EU. Included are introductions to the history of federalism in the USA and the EU, the current debates over devolution and subsidiarity, the legal framework of federalism and theories of regulatory federalism, as well as innovative approaches to the application of network analysis, principal‐agent models, institutionalist analysis, and political theories of citizenship to the federal context. The introduction and conclusion by the editors draws out cross‐cutting themes and lessons from the thinking together of the EU and USA experiences, and suggest how a ‘federal vision’ could be freed from the hierarchical paradigm of the ‘federal state’ and articulated around concepts of mutual tolerance and empowerment. The seventeen chapters are arranged in five sections: I. Articulating the Federal Vision (two chapters)—views of federalism in its USA and EU versions; II. Levels of Governance in the USA and the European Union: Facts and Diagnosis (four chapters)—an overview of the history and current state of federalism in the USA and EU; III. Legal and Regulatory Instruments of Federal Governance (three chapters); IV. Federalism, Legitimacy, and Governance: Models for Understanding (four chapters); V. Federalism, Legitimacy, and Identity (four chapters)—a discussion of the deeper roots of legitimacy in federal systems; there is also an appendix, which discusses the basic principles for the allocation of competence in the USA and EU.

Artificial intelligence (AI) and machine learning (ML) have made tremendous advancements in the past decades. From simple recommendation systems to more complex tumor identification systems, AI/ML systems have been utilized in a plethora of applications. This rapid growth of AI/ML and its proliferation in numerous private and public sector applications, while successful, has also opened new challenges and obstacles for regulators. With almost little to no human involvement required for some of the new decision-making AI/ML systems, there is now a pressing need to ensure the responsible use of these systems. Particularly in federal government use-cases, the use of AI technologies must be carefully governed by appropriate transparency and accountability mechanisms. This has given rise to new interdisciplinary fields of AI research such as \textit{Responsible AI (RAI)}. In this position paper we provide a brief overview of development in RAI and discuss some of the motivating principles commonly explored in the field. An overview of the current regulatory landscape relating to AI is also discussed with analysis of different Executive Orders, policies and frameworks. We then present ex

We examine how the federal government can enhance its AI emergency preparedness: the ability to detect and prepare for time-sensitive national security threats relating to AI. Emergency preparedness can improve the government's ability to monitor and predict AI progress, identify national security threats, and prepare effective response plans for plausible threats and worst-case scenarios. Our approach draws from fields in which experts prepare for threats despite uncertainty about their exact nature or timing (e.g., counterterrorism, cybersecurity, pandemic preparedness). We focus on three plausible risk scenarios: (1) loss of control (threats from a powerful AI system that becomes capable of escaping human control), (2) cybersecurity threats from malicious actors (threats from a foreign actor that steals the model weights of a powerful AI system), and (3) biological weapons proliferation (threats from users identifying a way to circumvent the safeguards of a publicly-released model in order to develop biological weapons.) We evaluate the federal government's ability to detect, prevent, and respond to these threats. Then, we highlight potential gaps and offer recommendations to im

This study examines the extent to which U.S. federal agencies responded to and implemented the principles outlined in the White House's October 2022 "Blueprint for an AI Bill of Rights." The Blueprint provided a framework for the ethical governance of artificial intelligence systems, organized around five core principles: safety and effectiveness, protection against algorithmic discrimination, data privacy, notice and explanation about AI systems, and human alternatives and fallback. Through an analysis of publicly available records across 15 federal departments, the authors found limited evidence that the Blueprint directly influenced agency actions after its release. Only five departments explicitly mentioned the Blueprint, while 12 took steps aligned with one or more of its principles. However, much of this work appeared to have precedents predating the Blueprint or motivations disconnected from it, such as compliance with prior executive orders on trustworthy AI. Departments' activities often emphasized priorities like safety, accountability and transparency that overlapped with Blueprint principles, but did not necessarily stem from it. The authors conclude that the non-bindin

Understanding how transportation networks work is important for improving connectivity, efficiency, and safety. In Brazil, where road transport is a significant portion of freight and passenger movement, network science can provide valuable insights into the structural properties of the infrastructure, thus helping decision makers responsible for proposing improvements to the system. This paper models the federal road network as weighted networks, with the intent to unveil its topological characteristics and identify key locations (cities) that play important roles for the country through 75,000 kilometres of roads. We start with a simple network to examine basic connectivity and topology, where weights are the distance of the road segment. We then incorporate other weights representing number of incidents, population, and number of cities in-between each segment. We then focus on community detection as a way to identify clusters of cities that form cohesive groups within a network. Our findings aim to bring clarity to the overall structure of federal roads in Brazil, thus providing actionable insights for improving infrastructure planning and prioritising resources to enhance netw

Digitalization is conquering and stressing out the federal administration. Using selected large-scale ICT projects, we show how complex and interdisciplinary the tasks are. The federal administration's IT strategy requires well-trained specialists for all defined fields of action. This scarce resource is increasingly being trained academically in separate, tailor-made degree courses that are developed specifically for the needs of the German ministries and authorities. We use the example of administrative informatics courses to explain their necessity and success story. Using a Bachelor's/Master's program developed by the authors for the ITZBund and the Federal Ministry of Finance, we look at a concrete implementation and justify two of our design decisions in the development of the course, namely transdisciplinarity and design thinking. We adopt a German perspective throughout the paper. However, the conclusions also apply to other countries.

This informative report provides a comprehensive analysis of how executive federal report agencies implement the National Institute of Standards and Technology's (NIST) Risk Management Framework (RMF) to achieve cybersecurity compliance. By exploring the concept and evolution of the RMF, the report delves into the framework's importance for enhancing cybersecurity measures within federal agencies, addressing the challenges these agencies face in the digital landscape. Through a methodical literature review, the report examines theoretical foundations, implementation strategies, and the critical role of continuous monitoring and automation in RMF processes, drawing from key sources like Ross (2014), Lubell (2020), Barrett et al. (2021), and Pillitteri et al. (2021, 2022), among others. Employing a detailed methodology for data collection and analysis, the report presents findings on the successes and challenges of RMF implementation, highlighting the impact of automation and continuous monitoring in bolstering cybersecurity postures. Case studies offer in-depth insights into the experiences of specific agencies, providing lessons learned and best practices. The report concludes with

As Canada prepares for the 2025 federal election, ensuring the integrity and security of the electoral process against cyber threats is crucial. Recent foreign interference in elections globally highlight the increasing sophistication of adversaries in exploiting technical and human vulnerabilities. Such vulnerabilities also exist in Canada's electoral system that relies on a complex network of IT systems, vendors, and personnel. To mitigate these vulnerabilities, a threat assessment is crucial to identify emerging threats, develop incident response capabilities, and build public trust and resilience against cyber threats. Therefore, this paper presents a comprehensive national cyber threat assessment, following the NIST Special Publication 800-30 framework, focusing on identifying and mitigating cybersecurity risks to the upcoming 2025 Canadian federal election. The research identifies three major threats: misinformation, disinformation, and malinformation (MDM) campaigns; attacks on critical infrastructure and election support systems; and espionage by malicious actors. Through detailed analysis, the assessment offers insights into the capabilities, intent, and potential impact o

On August 25, 2022, the White House Office of Science and Technology Policy (OSTP) released a memo regarding public access to scientific research. Signed by Director Alondra Nelson, this updated guidance eliminated the 12-month embargo period on publications arising from U.S. federal funding that had been allowed from a previous 2013 OSTP memo. While reactions to this updated federal guidance have been plentiful, to date there has not been a detailed analysis of the publications which would fall under this new framework. The OSTP released a companion report along with the memo, but it only provided a broad estimate of total numbers affected per year. Therefore, this study seeks to more deeply investigate the characteristics of U.S. federally funded research over a 5-year period from 2017-2021 to better understand the updated guidance's impact. It uses a manually created custom filter in the Dimensions database to return only publications that arise from U.S. federal funding. Results show that an average of 265,000 articles were published each year that acknowledge U.S. federal funding agencies, and these research outputs are further examined by publisher, journal title, institution

With the deepening of the digitization degree of financial business, financial fraud presents more complex and hidden characteristics, which poses a severe challenge to the risk prevention and control ability of financial institutions. At the same time, the vigorous development of big data technology provides massive potential information resources, and federated learning, as an emerging distributed machine learning paradigm, can realize multi-party data collaborative modeling under the premise of protecting data privacy. This paper firstly elaborates the basic principle, advantages and unique value of federated learning in solving data silos and protecting user privacy. Aiming at the needs of financial fraud detection, this paper discusses the design of federal learning architecture suitable for this scenario, including selecting suitable model type (such as neural network), setting reasonable data partitioning and updating rules. The central theme of the dissertation revolves around the exploration and execution of an algorithm for detecting financial fraud, which is grounded in federated learning methodologies. With a federated learning framework, each participant trains the mod

In recent years, the neural network backdoor hidden in the parameters of the federated learning model has been proved to have great security risks. Considering the characteristics of trigger generation, data poisoning and model training in backdoor attack, this paper designs a backdoor attack method based on federated learning. Firstly, aiming at the concealment of the backdoor trigger, a TrojanGan steganography model with encoder-decoder structure is designed. The model can encode specific attack information as invisible noise and attach it to the image as a backdoor trigger, which improves the concealment and data transformations of the backdoor trigger.Secondly, aiming at the problem of single backdoor trigger mode, an image poisoning attack method called combination trigger attack is proposed. This method realizes multi-backdoor triggering by multiplexing combined triggers and improves the robustness of backdoor attacks. Finally, aiming at the problem that the local training mechanism leads to the decrease of the success rate of backdoor attack, a dual model replacement backdoor attack algorithm based on federated learning is designed. This method can improve the success rate o

This paper examines the monetary policies the Federal Reserve implemented in response to the Global Financial Crisis. More specifically, it analyzes the Federal Reserve's quantitative easing (QE) programs, liquidity facilities, and forward guidance operations conducted from 2007 to 2018. The essay's detailed examination of these policies culminates in an interrupted time-series (ITS) analysis of the long-term causal effects of the QE programs on U.S. inflation and real GDP. The results of this formal design-based natural experimental approach show that the QE operations positively affected U.S. real GDP but did not significantly impact U.S. inflation. Specifically, it is found that, for the 2011Q2-2018Q4 post-QE period, real GDP per capita in the U.S. increased by an average of 231 dollars per quarter relative to how it would have changed had the QE programs not been conducted. Moreover, the results show that, in 2018Q4, ten years after the beginning of the QE programs, real GDP per capita in the U.S. was 14% higher relative to what it would have been during that quarter had there not been the QE programs. These findings contradict Williamson's (2017) informal natural experimental

As QKD infrastructure becomes increasingly complex while being developed by different actors (typically national governments), interconnecting them into a federated network of very elaborate sub-networks that maintain a high degree of autonomy will pose unique challenges. We identify several such challenges and propose a 4-step orchestration framework to address them based on centralized research, target network planning, optimal QKD design, and protocol enforcement.