Ethical and Social Aspects of Self-Driving Cars

As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.Comment: 11 pages, 3 figures, 2 table

Law, Metaphor, and the Encrypted Machine

The metaphors we use to imagine, describe, and regulate new technologies have profound legal implications. This article offers a critical examination of the metaphors we choose to describe encryption technology and aims to uncover some of the normative and legal implications of those choices. The article begins with a basic technical backgrounder and reviews the main legal and policy problems raised by strong encryption. Then it explores the relationship between metaphor and the law, demonstrating that legal metaphor may be particularly determinative wherever the law seeks to integrate novel technologies into old legal frameworks. The article establishes a loose framework for evaluating both the technological accuracy and the legal implications of encryption metaphors used by courts and lawmakers—from locked containers, car trunks, and combination safes to speech, shredded letters, untranslatable books, and unsolvable puzzles. What is captured by each of these cognitive models, and what is lost

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Is Google Duplex too human? : exploring user perceptions of opaque conversational agents

Conversational Agents (CAs) are increasingly embedded in consumer products, such as smartphones, home devices, and industry devices. Advancements in machine generated voice, such as the Google Duplex feature released in May 2018, aim to perfectly mimic the human voice while constructing a scenario in which users do not know whether they are talking to a human or a CA. Exactly how well users can distinguish between human/machine voices, how the degree of humanness impacts user emotional perception, and what ethical concerns this raises, remains an underexplored area. To answer these questions, I collected 405 surveys, including both an experimental design that exposed users to three different voices (human, advanced machine, and simple machine) and questions about the ethical implication of CAs. Results of the experiment revealed that users have difficulty distinguishing between human and advanced machine voices. Users do not experience the negative feeling referred to as the uncanny valley when listening to advanced synthetic audio and they only narrowly prefer a real human voice over a synthetic voice. Results from the questions about ethical implications revealed the importance of context and transparency. Drawing on these findings, I discuss the implications of advanced CAs and suggest strategies for ethical design.Journalis

Database and Data Mining in Social Networking

Today’s data driven world exploiting the latest trends of database and its allied technologies like Data Warehouse and Data Mining. Data Mining in recent years emerged as one of the most efficient database technique proved to be very reliable almost in every organisation enabling to find previously unknown hidden data patterns for the benefit of organisation. At the same time it is imposing serious problems concerned to data privacy and its potential misuse

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Mobile health systems and emergence

Changes in the age distribution of the population and increased prevalence of chronic illnesses, together with a shortage of health professionals and other resources, will increasingly challenge the ability of national healthcare systems to meet rising demand for services. Large-scale use of eHealth and mHealth services enabled by advances in ICT are frequently cited as providing part of the solution to this crisis in future provision. As part of this picture, self-monitoring and remote monitoring of patients, for example by means of smartphone apps and body-worn sensors, is on the way to becoming mainstream. In future, each individual’s personal health system may be able to access a large number of devices, including sensors embedded in the environment as well as in-body smart medical implants, in order to provide (semi-)autonomous health-related services to the user. This article presents some examples of mHealth systems based on emerging technologies, including body area networks (BANs), wireless and mobile technologies, miniature body-worn sensors and distributed decision support. Applications are described in the areas of management of chronic illnesses and management of (large- scale) emergency situations. In the latter setting BANs form part of an advanced ICT system proposed for future major incident management; including BANs for monitoring casualties and emergency services personnel during first response. Some challenges and possibilities arising from current and future emerging mHealth technologies, and the question of how emergence theory might have a bearing on understanding these challenges, is discussed here