In The Risk Perception of Artificial Intelligence, Hugo Neri examines how society has come to understand artificial intelligence by studying how cultural productions, intellectuals, and the media have shaped society’s views, understandings, and fears of artificial intelligence. As an abstract term, artificial intelligence has been understood both as a discipline and a "robot's mind." In the twenty and twenty-first centuries, cultural representations in comics, television shows, and movies converged with public lectures about the risks of A.I. by prominent public figures such as Stephen Hawking and Elon Musk. Neri analyzes how this cultural and intellectual miscellany shapes the way we perceive artificial intelligence and whether this perception is universal or restricted to the Western world.

Risk communication: the evolution of attempts Risk communication is at once a very new and a very old field of interest. Risk analysis, as Krimsky and Plough (1988:2) point out, dates back at least to the Babylonians in 3200 BC. Cultures have traditionally utilized a host of mecha nisms for anticipating, responding to, and communicating about hazards - as in food avoidance, taboos, stigma of persons and places, myths, migration, etc. Throughout history, trade between places has necessitated labelling of containers to indicate their contents. Seals at sites of the ninth century BC Harappan civilization of South Asia record the owner and/or contents of the containers (Hadden, 1986:3). The Pure Food and Drug Act, the first labelling law with national scope in the United States, was passed in 1906. Common law covering the workplace in a number of countries has traditionally required that employers notify workers about significant dangers that they encounter on the job, an obligation formally extended to chronic hazards in the OSHA's Hazard Communication regulation of 1983 in the United States. In this sense, risk communication is probably the oldest way of risk manage ment. However, it is only until recently that risk communication has attracted the attention of regulators as an explicit alternative to the by now more common and formal approaches of standard setting, insuring etc. (Baram, 1982).

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Featuring contributions from leading experts and thinkers in the theory of artificial intelligence (AI), this is one of the first books dedicated to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. The book covers the latest AI research, including the risks and future impacts. Ethical issues in AI are covered extensively along with an exploration of autonomous technology and its impact on humanity.

This book is the first to examine the history of imaginative thinking about intelligent machines, featuring contributions from leading humanities and social science scholars who detail the narratives about artificial intelligence (AI) that in turn offer a crucial epistemic site for exploring contemporary debates about these powerful technologies.

The study of risk perception started when experts in these matters figured out that common people often disagreed about how they envisage risk, even in how people observed that risk. There are many theories about risk perception. Some are exclusively related to risk perception while others are adaptations from perception theories. Many of these theories date back to last century, others are more recent. Chapter One of this book discusses the reasons there are many different theories of risk perception. Chapter Two studies community vulnerability in risk analysis task using multi-source data statistics. Chapter Three offers a thorough analysis of risk perception and psychosocial vulnerability in a health context, from a theoretical perspective, and based on current scientific evidence, in order to open research lines for both of these constructs, both separately and collectively. Chapter Four establishes a relationship between the consumption of drugs like cannabis, cocaine and designer drugs in young university and non-university populations with the young people's perception of risks associated with such consumption. Chapter Five provides an analysis of consumers' choice of pay television and car insurance services based on the perceived risk model.

In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.