The national security argument for protection of domestic industries

Tracing the origin of the national security argument for protection of domestic industries to Adam Smith, Alexander Hamilton, and Friedrich List, we study its post-GATT applications with reference to Article XXI of the WTO. We compare the use of tariff, production/input subsidy, and government procurement as alternative instruments of protection from the perspective of economic efficiency and study the disapproval of inward FDI to gain insights into the underlying national security concerns. The case studies of a) the US tariffs on aluminum and steel, b) German disapproval of the acquisition of a technology firm Leifeld Metal Spinning by a Chinese firm, and c) US’ all out global effort to cripple China’s telecom equipment giant Huawei are presented for illustration

AI Now Institute 2023 Landscape: Confronting Tech Power

This report highlights a set of approaches that, in concert, will collectively enable us to confront tech power. Some of these are bold policy reforms that underscore the need for bright-line rules and structural curbs. Others identify popular policy responses that, because they fail to meaningfully address power discrepancies, should be abandoned. Several aren't in the traditional domain of policy at all, but acknowledge the importance of nonregulatory interventions such as collective action, worker organizing, and the role public policy can play in bolstering these efforts. We intend this report to provide strategic guidance to inform the work ahead of us, taking a bird's eye view of the many levers we can use to shape the future trajectory of AI – and the tech industry behind it – to ensure that it is the public, not industry, that this technology serves

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

The Impact of Artificial Intelligence on Military Defence and Security

The twenty-first century is now being shaped by a multipolar system characterized by techno-nationalism and a post-Bretton Woods order. In the face of a rapidly evolving digital era, international cooperation will be critical to ensuring peace and security. Information sharing, expert conferences and multilateral dialogue can help the world's nation-states and their militaries develop a better understanding of one another's capabilities and intentions. As a global middle power, Canada could be a major partner in driving this effort. This paper explores the development of military-specific capabilities in the context of artificial intelligence (AI) and machine learning. Building on Canadian defence policy, the paper outlines the military applications of AI and the resources needed to manage next-generation military operations, including multilateral engagement and technology governance

From P4 medicine to P5 medicine: transitional times for a more human-centric approach to AI-based tools for hospitals of tomorrow

Within the debate on shaping future clinical services, where different robotics and artificial intelligence (AI) based technologies are integrated to perform tasks, the authors take the chance to provide an interdisciplinary analysis required to validate a tool aiming at supporting the melanoma cancer diagnosis. In particular, they focus on the ethical-legal and technical requirements needed to address the Assessment List on Trustworthy AI (ALTAI), highlighting some pros and cons of the adopted self-assessment checklist. The dialogue stimulates additionally remarks on the EU regulatory initiatives on AI in the healthcare systems

THE ETHICAL USE OF FACIAL RECOGNITION TECHNOLOGY: A CASE STUDY OF U.S. CUSTOMS AND BORDER PROTECTION

After the events of 9/11, facial recognition technology (FRT) emerged as a security solution for identifying and verifying individuals in a homeland security setting. Although FRT demonstrates security benefits, the public has not widely accepted the government’s use of the technology. FRT critics raise ethical and societal concerns regarding the negative impact of the technology on the public, including privacy concerns, constitutional rights violations, biased and inaccurate technology, and data management. How can FRT be implemented in a way that is both efficient and ethical? This thesis analyzes FRT through a three-pronged approach. First, the thesis applies the “How to Do It Right” ethical framework to a government agency’s decision-making process. The second step identifies ethical operating principles through a crosswalk of the varied and often inconsistent operating principles published by the security industry, government audit agencies, and watchdog groups. Finally, the thesis utilizes a real-world case study to explore an operational FRT program and illustrate best practices. It recommends that following an ethical framework during decision-making and incorporating ethical principles and best practices into FRT programs during development and implementation mitigates the public’s ethical and societal concerns.Civilian, Department of Homeland SecurityApproved for public release. Distribution is unlimited

Smart Borders or a Humane World?

On January 20, 2021, his first day in office, President Biden issued an executive order pausing the remaining construction of the southern border wall initiated during the Trump administration. Soon after, the White House sent a bill to Congress, the US Citizenship Act of 2021, calling for the deployment of "smart technology" to "manage and secure the southern border."This report delves into the rhetoric of "smart borders" to explore their ties to a broad regime of border policing and exclusion that greatly harms migrants and refugees who either seek or already make their home in the United States. Investment in an approach centered on border and immigrant policing, it argues, is incompatible with the realization of a just and humane world.The report concludes by arguing that we must move beyond a narrow debate limited to "hard" versus "smart" borders toward a discussion of how we can move toward a world where all people have the support needed to lead healthy, secure, and vibrant lives. A just border policy would ask questions such as: How do we help create conditions that allow people to stay in the places they call home, and to thrive wherever they reside? When people do have to move, how can we ensure they are able to do so safely? When we take these questions as our starting point, we realize that it is not enough to fix a "broken" system. Rather, we need to reimagine the system entirely

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks