AI Governance

The United States faces an escalating array of public safety challenges, from violent crime and mass shootings to increasingly severe climate-related disasters. Between 1980 and 2024, the U.S. experienced 387 weather and climate disasters, each with damages surpassing $1 billion, amounting to over $2.74 trillion in costs. Similarly, mass shootings and gun violence reached an alarming frequency, with more than 630 mass shooting incidents recorded by the Gun Violence Archive (GVA) in 2023 alone. The National Interagency Fire Center (NIFC) also reported 56,580 wildfires that burned over 2.7 million acres nationwide in the same year. These figures highlight the pressing need for robust, realtime public safety and emergency management systems to better predict, respond to, and mitigate such incidents. This paper explores the transformative potential of Artificial Intelligence (AI) in enhancing public safety and emergency management in the U.S. By leveraging AI-driven predictive analytics, machine learning (ML) models, and advanced threat detection algorithms, this study proposes an integrated approach to optimizing crime prevention, disaster response, and public safety operations. The research builds upon recent advancements in predictive analytics, deep learning, and cybersecurity measures. Through the analysis of real-time data from crime statistics, social media, IoT sensors, and environmental conditions, this study aims to demonstrate how AI can reduce response times, anticipate public safety threats, and allocate resources more effectively. The study will present case studies on the practical application of AI in real-time crime prediction, disaster management, and threat detection, showcasing how law enforcement, emergency management, and cybersecurity teams can use AI to shift from reactive responses to proactive, intelligence-driven operations. Moreover, it will address ethical concerns around AI deployment in public safety, such as privacy, algorithmic bias, and data governance, proposing frameworks for ensuring responsible use of these technologies. The ultimate goal is to offer a strategic roadmap for integrating AI into U.S. public safety infrastructure, enhancing preparedness, and protecting communities from the growing risks of violent crime, natural disasters, and emerging threats.

Artificial Intelligence raises new, distinct governance challenges, as well as familiar governance challenges in novel ways. The governance of AI, moreover, is not an issue of distant futures, it is well underwayand it has characteristics akin to 'herding cats' with a mind of their own. This essay introduces the contributions to the special issue, situating them in broader political and social science literatures. It then provides a sketch of an interdisciplinary research agenda. It highlights the limits of 'explainable AI', makes the case for considering AI ethics and AI governance simultaneously, identifies as an underappreciated risk 'system effects' that arise from the introduction of AI applications, and calls for policymakers to consider both the opportunities and the risks of AI. Focusing on the (ab)uses of AI, rather than the complex, rapidly changing and hard-to-predict technology as such, might provide a superior approach to governing AI.

Recent advances in artificial intelligence (AI), combined with a proliferation in use, have led to a newfound emphasis on strengthening the global governance of AI. In this article, we assess the prospects for stronger global AI governance and consider potential pathways forward. We map the nascent landscape of international regimes focused on AI governance and conclude that a governance deficit remains due to the inadequacy of existing initiatives, gaps in the landscape and difficulties reaching agreement over more appropriate mechanisms. First-order cooperation problems stemming from interstate competition and second-order cooperation problems from dysfunctional international institutions problematise overcoming current deficiencies in global AI governance. In light of these cooperation problems, we evaluate two pathways for strengthening global AI governance: (a) developing new centralised international AI institution(s) and (b) strengthening coordination between, and capacities of, existing institutions. We argue that strengthening the existing weak 'regime complex' of international institutions is the more politically legitimate and viable path forward. Improving coordination between, and capacities of, existing international institutions governing AI would support inclusive and mutually reinforcing policy change that can mitigate a range of risks associated with these technologies. As first steps for strengthening the weak AI regime complex, we recommend that the OECD is foregrounded as a centre of expert AI knowledge so that it can facilitate peer pressure among states and harmonisation of policies. We also encourage scrutiny of different nodes in the regime complex to ensure that they are fulfilling appropriate functions based on their democratic mandates.

Adopting Artificial Intelligence (AI) in electric utilities signifies vast, yet largely untapped potential for accelerating a clean energy transition. This requires tackling complex challenges such as trustworthiness, explainability, privacy, cybersecurity, and governance, balancing these against AI's benefits. This article aims to facilitate dialogue among regulators, policymakers, utilities, and other stakeholders on navigating these complex issues, fostering a shared understanding and approach to leveraging AI's transformative power responsibly. The complex interplay of state and federal regulations necessitates careful coordination, particularly as AI impacts energy markets and national security. Promoting data sharing with privacy and cybersecurity in mind is critical. The article advocates for 'realistic open benchmarks' to foster innovation without compromising confidentiality. Trustworthiness (the system's ability to ensure reliability and performance, and to inspire confidence and transparency) and explainability (ensuring that AI decisions are understandable and accessible to a large diversity of participants) are fundamental for AI acceptance, necessitating transparent, accountable, and reliable systems. AI must be deployed in a way that helps keep the lights on. As AI becomes more involved in decision-making, we need to think about who's responsible and what's ethical. With the current state of the art, using generative AI for critical, near real-time decision-making should be approached carefully. While AI is advancing rapidly both in terms of technology and regulation, within and beyond the scope of energy specific applications, this article aims to provide timely insights and a common understanding of AI, its opportunities and challenges for electric utility use cases, and ultimately help advance its adoption in the power system sector, to accelerate the equitable clean energy transition.

The current political and regulatory discourse frequently references the term "trustworthy artificial intelligence (AI)." In Europe, the attempts to ensure trustworthy AI started already with the High-Level Expert Group Ethics Guidelines for Trustworthy AI and have now merged into the regulatory discourse on the EU AI Act. Around the globe, policymakers are actively pursuing initiatives-as the US Executive Order on Safe, Secure, and Trustworthy AI, or the Bletchley Declaration on AI showcase-based on the premise that the right regulatory strategy can shape trust in AI. To analyze the validity of this premise, we propose to consider the broader literature on trust in automation. On this basis, we constructed a framework to analyze 16 factors that impact trust in AI and automation more broadly. We analyze the interplay between these factors and disentangle them to determine the impact regulation can have on each. The article thus provides policymakers and legal scholars with a foundation to gauge different regulatory strategies, notably by differentiating between those strategies where regulation is more likely to also influence trust on AI (e.g., regulating the types of tasks that AI may fulfill) and those where its influence on trust is more limited (e.g., measures that increase awareness of complacency and automation biases). Our analysis underscores the critical role of nuanced regulation in shaping the human-automation relationship and offers a targeted approach to policymakers to debate how to streamline regulatory efforts for future AI governance.