The Digital Person: Technology and Privacy in the Information Age Daniel J. Solove. New York: New York University Press, 2006. 290 pp. $29.95.Daniel J. Solove, like most contemporary writers on privacy, offers a sky-is-falling perspective on privacy in the modern paperless, transactional age. Due in part to the rise of so-called "digital dossiers" and perhaps, in the opinion of the reviewer, due in part to apathy, Solove observes recent developments as a new paradigm of the privacy problematic. The goal is "to rethink longstanding notions of privacy to grapple with the consequences of living in an information age" (p. 2). Much of what is covered here is not new. As Solove comments in his introduction: "There are hundreds of companies that are constructing gigantic databases of psychological profiles, amassing data about an individual's race, gender, income, hobbies, and purchases" (p. 2). Although the combination of private and public information "has long been viewed as problematic" (p. 6), Solove never adequately explains why or moves beyond the obvious: Marketing, credit, and related research companies have been engaging in such practices for years; in fairness to Solove, perhaps the difference is capacity to collect, to share, to compile, etc. This is the power of "aggregation" which contributes to the inability to assign adequate value to personal information. Though Solove does not make the connection, this may be a key to understanding the power of apathy or more kindly the unawareness that immobilizes individual responses to personal privacy protection. What is new is that "beyond articulating a new understanding of contemporary privacy problems" Solove attempts "to demonstrate the ways that the problems can be solved" (p. 6). After observing traditional conceptualizations such as big brother (Orwellian, pp. 29-35), secrecy and invasion (though he uses the idea of invasion to describe both), Solove claims to use a new Kafkaesque metaphor of irresponsible bureaucracy (pp. 36-41).On the way to this metaphor, Chapter 2 recounts the rise of public and private sector databases and the new uses of the web as a point and source of data collection. Chapter 3 reviews various metaphors and views the ultimate harm as one affecting human dignity through misjudgments, diminished capacity to participate, and unfairness (perhaps unevenness is more descriptive) in the collection of information. Chapter 4 reviews the inadequacy of the private (traditional tort and more recent statutory sector by sector approaches) and public law of privacy in the United States. …

The Digital Person: Technology and Privacy in the Information Age

This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Unlike existing review papers, we examine the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm-driven and data-driven cars. In this article, we aim to bridge the gap between deep learning and self-driving cars through a comprehensive survey. We begin with an introduction to self-driving cars, deep learning, and computer vision followed by an overview of artificial general intelligence. Then, we classify existing powerful deep learning libraries and their role and significance in the growth of deep learning. Finally, we discuss several techniques that address the image perception issues in real-time driving, and critically evaluate recent implementations and tests conducted on self-driving cars. The findings and practices at various stages are summarized to correlate prevalent and futuristic techniques, and the applicability, scalability and feasibility of deep learning to self-driving cars for achieving safe driving without human intervention. Based on the current survey, several recommendations for further research are discussed at the end of this article.

Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues

Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

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Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Environmental perception technology is the guarantee of the safety of driverless vehicles. At present, there are a lot of researches and reviews on environmental perception, aiming to realize unmanned driving while ensuring the safety of human life. However, the technology is facing new challenges in the new era. This review paper attempts to systematically summarize environment perception technology and discuss the new challenges currently faced. To this end, we first summarized the advantages, disadvantages and applicable occasions of several commonly used sensing methods to provide a clear selection guide. The new challenges faced by environmental perception technology are discussed from three aspects: technology, external environment and applications. Finally, the article also points out the future development trends and efforts of environmental perception technology.

Sensing system of environmental perception technologies for driverless vehicle: A review of state of the art and challenges

Find loads of the encyclopedia of information ethics and security book catalogues in this site as the choice of you visiting this page. You can also join to the website book library that will show you numerous books from any types. Literature, science, politics, and many more catalogues are presented to offer you the best book to find. The book that really makes you feels satisfied. Or that's the book that will save you from your job deadline.

Encyclopedia of Information Ethics and Security

With the advent of autonomous vehicles society will need to confront a new set of risks which, for the first time, includes the ability of socially embedded forms of artificial intelligence to make...

https://tandfonline.com/doi/pdf/10.1080/08839514.2019.1600301

Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions

The provision of an adequate liability regime for ADAS technologies is an essential prerequisite for its roll out over the coming decade. Facing to the challenge of future highly automated vehicles, this paper proposed a Human-Machine Transition (HMT) approach as a common conceptual framework for considering Human Machine Interaction (HMI), liability and ethical issues in a unified way. The issues that arise are interrogated from a legal perspective, more specifically liability regimes and that of applied ethics. The paper highlights the issue of the handover/takeover. Potential consequences for insurance companies are then identified accordingly, with the aim to progress towards the sustainable deployment of automated vehicles on public roads.

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From semi to fully autonomous vehicles: New emerging risks and ethico-legal challenges for human-machine interactions

This article aims to introduce a degree of technological and ethical realism to the framing of autonomous vehicle perception and decisionality. The objective is to move the socioethical dia...

Autonomous vehicles and avoiding the trolley (dilemma): vehicle perception, classification, and the challenges of framing decision ethics

The question of the capacity of artificial intelligence to make moral decisions has been a key focus of investigation in robotics for decades. This question has now become pertinent to auto...

Artificial Driving Intelligence and Moral Agency: Examining the Decision Ontology of Unavoidable Road Traffic Accidents through the Prism of the Trolley Dilemma

Our social relations are changing, we are now not just talking to each other, but we are now also talking to artificial intelligence (AI) assistants. We claim AI assistants present a new form of digital connectivity risk and a key aspect of this risk phenomenon is related to user risk awareness (or lack of) regarding AI assistant functionality. AI assistants present a significant societal risk phenomenon amplified by the global scale of the products and the increasing use in healthcare, education, business, and service industry. However, there appears to be little research concerning the need to not only understand the changing risks of AI assistant technologies but also how to frame and communicate the risks to users. How can users assess the risks without fully understanding the complexity of the technology? This is a challenging and unwelcome scenario. AI assistant technologies consist of a complex ecosystem and demand explicit and precise communication in terms of communicating and contextualising the new digital risk phenomenon. The paper then argues for the need to examine how to best to explain and support both domestic and commercial user risk awareness regarding AI assistants. To this end, we propose the method of creating a risk narrative which is focused on temporal points of changing societal connectivity and contextualised in terms of risk. We claim the connectivity risk narrative provides an effective medium in capturing, communicating, and contextualising the risks of AI assistants in a medium that can support explainability as a risk mitigation mechanism.

/pdf/artificial-intelligence-assistants-and-risk-framing-a-4ffibhvl9e.pdf

Martin Cunneen

Papers

Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions

From semi to fully autonomous vehicles: New emerging risks and ethico-legal challenges for human-machine interactions

Autonomous vehicles and avoiding the trolley (dilemma): vehicle perception, classification, and the challenges of framing decision ethics

Artificial Driving Intelligence and Moral Agency: Examining the Decision Ontology of Unavoidable Road Traffic Accidents through the Prism of the Trolley Dilemma

Artificial intelligence assistants and risk: framing a connectivity risk narrative