Pervasive Tracking Research Articles

Growing Up with Digital Technologies: How the Precautionary Principle Might Contribute to Addressing Potential Serious Harm to Children’s Rights

ABSTRACT Digital technologies affect the lives of children and the rights that are specifically attributed to them by the United Nations Convention on the Rights of the Child (UNCRC), article 24 of the EU Charter of Fundamental Rights and many national constitutions. There is no doubt that the digital environment has enormous potential for the empowerment of children and their exercise of the UNCRC rights. Yet, children (and their parents) are expected to make difficult decisions about whether or not or how to use platforms, devices and services that function in complex and opaque manners and whose impact on the rights of the child are difficult to grasp. This is especially the case for data- and advertising-driven practices that intentionally deceive or manipulate children or that are based on pervasive tracking and inferences about children's behaviour, attention and emotions. Based on a legal study of the UNCRC, legislation and policy documents at UN, Council of Europe and European Union level, and scholarship, this article studies the extent to which duties and responsibilities rest on policymakers, platforms and service providers and regulators to uphold the best interests of the child as a primary consideration (Article 3 (1) UNCRC) and what this means for the digital spaces in which children grow up. In particular, the article explores how the application of the precautionary principle might better serve the best interests of the child and how this might require legal restrictions on certain practices, enhanced responsibilities for public and private actors, and stronger enforcement by regulators.

Being Human in an Algorithmically Controlled World

Most people in developed countries, and many in developing countries, interact with Artificial Intelligence (AI) developed algorithms on an almost daily basis, yet very few are aware of those interactions or of their effect upon people's daily lives. 1 Using machine learning, automated reasoning, and other forms of AI, algorithms deployed in information systems take actions according to criteria set up by software developers to maximize profits regardless of the overall effects on the autonomy or welfare of individuals or on society as a whole. 2 Those criteria almost always involve location-based and place-based data. In this paper, we consider effects of those computational tools on individual choice and autonomy and on societal structure from the humanistic perspectives of philosophy and jurisprudence. Specifically, we: • review contemporaneous literature and quantitative economic evidence on the effects of pervasive tracking and algorithmic controls on individuals and on society in general, • summarize a range of suggested solutions for lessening the adverse effects, and • describe and expand upon selected legal solutions from the literature.

GyroWand: An Approach to IMU-Based Raycasting for Augmented Reality.

Optical see-through head-mounted displays enable augmented reality (AR) applications that display virtual objects overlaid on the real world. At the core of this new generation of devices are low-cost tracking technologies that allow us to interpret users' motion in the real world in relation to the virtual content for the purposes of navigation and interaction. The advantages of pervasive tracking come at the cost of limiting interaction possibilities, however. To address these challenges the authors introduce GyroWand, a raycasting technique for AR HMDs using inertial measurement unit (IMU) rotational data from a handheld controller.

Algorithmic states of exception

In this article, I argue that pervasive tracking and data-mining are leading to shifts in governmentality that can be characterised as algorithmic states of exception. I also argue that the apparatus that performs this change owes as much to everyday business models as it does to mass surveillance. I look at technical changes at the level of data structures, such as the move to NoSQL databases, and how this combines with data-mining and machine learning to accelerate the use of prediction as a form of governance. The consequent confusion between correlation and causation leads, I assert, to the creation of states of exception. I set out what I mean by states of exception using the ideas of Giorgio Agamben, focusing on the aspects most relevant to algorithmic regulation: force-of and topology. I argue that the effects of these states of exception escape legal constraints such as concepts of privacy. Having characterised this as a potentially totalising change and an erosion of civil liberties, I ask in what ways the states of exception might be opposed. I follow Agamben by drawing on Walter Benjamin’s concept of pure means as a tactic that is itself outside the frame of law-producing or law-preserving activity. However, the urgent need to respond requires more than a philosophical stance, and I examine two examples of historical resistance that satisfy Benjamin’s criteria. For each, in turn, I draw connections to contemporary cases of digital dissent that exhibit some of the same characteristics. I conclude that it is possible both theoretically and practically to resist the coming states of exception, and I end by warning what is at stake if we do not.

Why Big Data Won't Cure Us.

The biggest challenge for the use of “big data” in health care is social, not technical. Data-intensive approaches to medicine based on predictive modeling hold enormous potential for solving some of the biggest and most intractable problems of health care. The challenge now is figuring out how people, both patients and providers, will actually use data in practice. To understand how data-intensive solutions could have an impact on health care, our research team talked to frontline providers in impoverished and rural areas, technology enthusiasts in mobile health and health IT startups, clinicians and researchers in major research hospitals, Quantified Self members at data-driven meetup presentations of massive amounts of tracking data, and attendees at the growing number of conferences for health technology and innovation up and down both coasts. I found the buzz as feverishly loud around health information innovation as it was during my research on the first dot-com boom. One of our findings from this research seems at first blush so obvious that it is hard to believe it has been overlooked in the design and implementation of health-care innovation technologies. Namely, people imagine data in very different ways. Understanding this key fact about data helps us understand why so-called “big data” solutions to health care are so difficult to implement in practice. Doctors, patients, and health-care entrepreneurs all value data in very different ways. One physician simply said, “I don't need more data; I need more resources.”* Saying this in Silicon Valley or at TedMed would be tantamount to heresy. Ditto for those of us who work in research and spend our careers collecting, massaging, managing, analyzing, and interpreting data. From the doctor's perspective, though, data require (and do not save) extra interpretive, clerical, and managerial labor. This perspective on “data,” at least with regard to current clinical practice, is that data use up more resources than the benefits they provide. In other words, most doctors think data innovation means more work for them, not less, and takes away time from what they see as their key priorities in providing quality care. In another setting, we observed nurse-practitioner case managers in a Medicare demonstration project working with a simple algorithm parsing patient-entered health data. Combined with case management, these data provided a look into the daily health of chronically ill elderly patients and a pathway for the care when it was needed. The data in that project were tightly tied to medical expertise within an existing clinic where a trusted person could initiate a chain of care responses. Although widely recognized as a clinical success, Medicare pulled the plug on the project for financial reasons—expertise is expensive. These two reactions to data-intensive pilot projects highlight the dilemmas of data-analytic approaches to health care. Businesses are in the thrall of the possibilities of ever-increasing predictive analysis on expanding troves of generated data. While the business and technology sectors see data as valuable, doctors often see data as costs, risks, and liabilities. And for many in health care, data are not seen as a source of value, but of additional work. Without the work needed to make data valuable and useful in particular settings in particular contexts in health care, big data will never solve problems. To turn a technology truism on its head, data in health care will never be free. And yet, the ways in which health technology innovators have talked about the power of data neglects key aspects of the social interoperability or integration of data into health solutions. How will such data be integrated into care providers' work practices; through the complex routines of clinics and hospitals; and into existing legal, social, political, and economic frameworks? These questions are enormous. Until we solve these questions of social interoperability, the risks presented by “big data” in health will outweigh the benefits to any particular individual, regardless of whether we're talking about terabyte-scale analytics or the “small-data” of n=1 individuals. What follows is an outline of how to tackle these questions based upon what our team has seen throughout our research.

Adaptive training of video sets for image recognition on mobile phones

We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera-equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client–server-system for improving data acquisition and for supporting scale-invariant object recognition. A static as well as a dynamic training technique are presented that preprocess the collected object data differently and apply two types of neural networks (NN) for classification. Furthermore, the system enables a temporal adaptation for ensuring a continuous data acquisition to improve the recognition rate over time. A formal field experiment reveals current recognition rates and indicates the practicability of both methods under realistic conditions in a museum.

Enabling Mobile Phones To Support Large-Scale Museum Guidance

We present a museum guidance system called PhoneGuide that uses widespread camera-equipped mobile phones for on-device object recognition in combination with pervasive tracking. It also provides location- and object-aware multimedia content to museum visitors, and is scalable to cover a large number of museum objects.