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In recent years, many sectors have experienced significant progress in automation, associated with the growing advances in artificial intelligence and machine learning. There are already automated robotic weapons, which are able to evaluate and engage with targets on their own, and there are already autonomous vehicles that do not need a human driver. It is argued that the use of increasingly autonomous systems (AS) should be guided by the policy of human control, according to which humans should execute a certain significant level of judgment over AS. While in the military sector there is a fear that AS could mean that humans lose control over life and death decisions, in the transportation domain, on the contrary, there is a strongly held view that autonomy could bring significant operational benefits by removing the need for a human driver. This article explores the notion of human control in the United States in the two domains of defense and transportation. The operationalization of emerging policies of human control results in the typology of direct and indirect human controls exercised over the use of AS. The typology helps to steer the debate away from the linguistic complexities of the term “autonomy.” It identifies instead where human factors are undergoing important changes and ultimately informs about more detailed rules and standards formulation, which differ across domains, applications, and sectors.
As computational power, the volume of available data, IT systems’ autonomy, and the human-like capabilities of machines increase, robots and AI systems have substantial and growing implications for the law and raise a host of challenges to current legal doctrines. The main question to be answered is whether the foundations and general principles of private law and criminal law offer a functional and adaptive legal framework for the “autonomous systems” phenomena. The main purpose of this book is to identify and explore possible trajectories for the development of civil and criminal liability; for our understanding of the attribution link to autonomous systems; and, in particular, for the punishment of unlawful conduct in connection with their operation. AI decision-making processes – including judicial sentencing – also warrant close attention in this regard. Since AI is moving faster than the process of regulatory recalibration, this book provides valuable insights on its redesign and on the harmonization, at the European level, of the current regulatory frameworks, in order to keep pace with technological changes. Providing a broader and more comprehensive picture of the legal challenges posed by autonomous systems, this book covers a wide range of topics, including the regulation of autonomous vehicles, data protection and governance, personality rights, intellectual property, corporate governance, and contract conclusion and termination issues arising from automated decisions, blockchain technology and AI applications, particularly in the banking and finance sectors. The authors are legal experts from around the world with extensive academic and/or practical experience in these areas.
Expounding on the results of the author's work with the US Army Research Office, DARPA, the Office of Naval Research, and various defense industry contractors, Governing Lethal Behavior in Autonomous Robots explores how to produce an "artificial conscience" in a new class of robots, humane-oids, which are robots that can potentially perform more et
This examination of the implications and regulation of autonomous weapons systems combines contributions from law, robotics and philosophy.
This open access book aims to set an agenda for research and action in the field of Digital Humanism through short essays written by selected thinkers from a variety of disciplines, including computer science, philosophy, education, law, economics, history, anthropology, political science, and sociology. This initiative emerged from the Vienna Manifesto on Digital Humanism and the associated lecture series. Digital Humanism deals with the complex relationships between people and machines in digital times. It acknowledges the potential of information technology. At the same time, it points to societal threats such as privacy violations and ethical concerns around artificial intelligence, automation and loss of jobs, ongoing monopolization on the Web, and sovereignty. Digital Humanism aims to address these topics with a sense of urgency but with a constructive mindset. The book argues for a Digital Humanism that analyses and, most importantly, influences the complex interplay of technology and humankind toward a better society and life while fully respecting universal human rights. It is a call to shaping technologies in accordance with human values and needs.
This prescient Research Handbook analyses the ethical development of Artificial Intelligence systems through the prism of meaningful human control. It encapsulates a multitude of disciplinary lenses including technical, philosophical and legal, making a crucial contribution to the ongoing discourse about control and responsibility in the field of AI.
This book examines recent advances in theories, models, and methods relevant to automated and autonomous systems. The following chapters provide perspectives on modern autonomous systems, such as self-driving cars and unmanned aerial systems, directly from the professionals working with and studying them. Current theories surrounding topics such as vigilance, trust, and fatigue are examined throughout as predictors of human performance in the operation of automated systems. The challenges related to attention and effort in autonomous vehicles described within give credence to still-developing methods of training and selecting operators of such unmanned systems. The book further recognizes the need for human-centered approaches to design; a carefully crafted automated technology that places the "human user" in the center of that design process. Features Combines scientific theories with real-world applications where automated technologies are implemented Disseminates new understanding as to how automation is now transitioning to autonomy Highlights the role of individual and team characteristics in the piloting of unmanned systems and how models of human performance are applied in system design Discusses methods for selecting and training individuals to succeed in an age of increasingly complex human-machine systems Provides explicit benchmark comparisons of progress across the last few decades, and identifies future prognostications and the constraints that impinge upon these lines of progress Human Performance in Automated and Autonomous Systems: Current Theory and Methods illustrates the modern scientific theories and methods to be applied in real-world automated technologies.
Kevin M. Passino When confronted with a control problem for complicated physical process, a control engineer usually follows a predetermined design procedure. This procedure often begins with the engineer seeking to understand the process and the primary control objectives. A simple example ofa control problem is an automobile "cruise control" that provides the automobile with the capability of regulating its own speed at a driver-specified set-point (e. g. , 55 mph). One solution to the automotive cruise control problem involves adding an electronic controller that can sense the speed of the vehicle via the speedometer and actuate the throttle position so as to regulate the vehicle speed at the driver-specified value. Such speed regulation must be accurate even if there are road grade changes, head-winds, or variations in the number of passengers in the automobile. After gaining an intuitive understanding of the plant's dynamics and establishing the design objectives, the control engineer typically solves the cruise control problem by using an established design procedure. In particular, this control engineering design methodology involves: 1. Modeling/understanding the plant, 2. Construction of a controller to meet specifications (such as stability, rise-time, overshoot, and steady state error), 3. Analysis to make sure that the system will meet the performance objectives (e. g. , we might use mathematical, simulation-based, or experimental analysis), and 4. Iterating on the design until it is possible to "commission" the control system.
Should we regulate artificial intelligence? Can we? From self-driving cars and high-speed trading to algorithmic decision-making, the way we live, work, and play is increasingly dependent on AI systems that operate with diminishing human intervention. These fast, autonomous, and opaque machines offer great benefits – and pose significant risks. This book examines how our laws are dealing with AI, as well as what additional rules and institutions are needed – including the role that AI might play in regulating itself. Drawing on diverse technologies and examples from around the world, the book offers lessons on how to manage risk, draw red lines, and preserve the legitimacy of public authority. Though the prospect of AI pushing beyond the limits of the law may seem remote, these measures are useful now – and will be essential if it ever does.