The explosion of data grows at a rate of roughly five trillion bits a second, giving rise to greater urgency in conceptualizing the infosphere and understanding its implications for knowledge and public policy. Philosophers of technology and information technologists alike who wrestle with ontological and epistemological questions of digital information tend to emphasize, as Floridi does, information as our new ecosystem and human beings as interconnected informational organisms, inforgs at home in ambient intelligence. But the linguistic and conceptual representations of (...) Big Data—the massive volume of both structured and unstructured data—and the real world practice of data-mining for patterns and meaningful interpretation of evidence reveal tension and ambiguity in the bold promise of data analytics. This paper explores the tacit epistemology of the rhetoric and representation of Big Data and suggests a richer account of its ambiguities and the paradox of its real world materiality. We argue that Big Data should be recognized as manifesting multiple and conflicting trajectories that reflect human intentionality and particular patterns of power and authority. Such patterns require attentive exploration and moral appraisal if we are to resist simplistic informationist ontologies of Big Data, and the subtle forms of control in the political ecology of Big Data that undermine its promise as transformational knowledge. (shrink)

The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications (...) of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

Persuasive design draws on our basic psychological makeup to build products that make our engagement with them habitual. It uses variable rewards, creates Fear of Missing Out, and leverages social approval to incrementally increase and maintain user engagement. Social media and networking platforms, video games, and slot machines are all examples of persuasive technologies. Recent attention has focused on the dangers of PD: It can deceptively prod users into forming habits that help the company’s bottom line but not the user’s (...) wellbeing. But PD is not inherently immoral. We can take advantage of our psychological biases to make beneficial changes in ways that enhance our agency rather than limit it. Knowing that a tool is persuasively designed is a consideration in favor of using it when we are trying to break bad habits, such as smoking. How are we to conceptually distinguish between ethical and unethical uses of PD? In this paper, I argue that unethical uses of PD betray or erode our trust. Annette Baier offers a moral test for trust: If gaining knowledge about what other parties do with our trust in them would lead us to stop trusting, then that trusting relationship is immoral. I apply this test to the case of PD. Using trust as a litmus test for ferreting out unethical PD has several advantages, one of which is that it reveals how the harm of unethical PD extends beyond the individual to her wider social network. I close the paper by investigating these cascading effects. (shrink)

Surveillance is essential for communicable disease prevention and control. Traditional notification of demographic and clinical information, about individuals with selected infectious diseases, allows appropriate public health action and is protected by public health and privacy legislation, but is slow and insensitive. Big data–based electronic surveillance, by commercial bodies and government agencies, which draws on a plethora of internet- and mobile device–based sources, has been widely accepted, if not universally welcomed. Similar anonymous digital sources also contain syndromic information, which can be (...) analysed, using customised algorithms, to rapidly predict infectious disease outbreaks, but the data are nonspecific and predictions sometimes misleading. However, public health authorities could use these online sources, in combination with de-identified personal health data, to provide more accurate and earlier warning of infectious disease events—including exotic or emerging infections—even before the cause is confirmed, and allow more timely public health intervention. Achieving optimal benefits would require access to selected data from personal electronic health and laboratory records and the potential to re-identify individuals found to be involved in outbreaks, to ensure appropriate care and infection control. Despite existing widespread digital surveillance and major potential community benefits of extending its use to communicable disease control, there is considerable public disquiet about allowing public health authorities access to personal health data. Informed public discussion, greater transparency and an ethical framework will be essential to build public trust in the use of new technology for communicable disease control. (shrink)