Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the (...) right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018. (shrink)

Algorithms have developed into somewhat of a modern myth. On the one hand, they have been depicted as powerful entities that rule, sort, govern, shape, or otherwise control our lives. On the other hand, their alleged obscurity and inscrutability make it difficult to understand what exactly is at stake. What sustains their image as powerful yet inscrutable entities? And how to think about the politics and governance of something that is so difficult to grasp? This editorial essay provides a critical (...) backdrop for the special issue, treating algorithms not only as computational artifacts but also as sensitizing devices that can help us rethink some entrenched assumptions about agency, transparency, and normativity. (shrink)

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To (...) achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’. (shrink)

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we (...) provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat (...) to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions. It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation. (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)

"Is there any such thing as political philosophy?" So begins this provocative book by one of the foremost figures in Continental thought. Here, Jacques Ranciere brings a new and highly useful set of terms to the vexed debate about political effectiveness in the face of a new world order. What precisely is at stake in the relationship between "philosophy" and the adjective "political"? In Disagreement, Ranciere explores the apparent contradiction between these terms and reveals the uneasy meaning of their union (...) in the phrase "political philosophy" -- a juncture related to age-old attempts in philosophy to answer Plato's devaluing of politics as a "democratic egalitarian" process. According to Ranciere, the phrase also expresses the paradox of politics itself: the absence of a proper foundation. Politics, he argues, begins when the "demos" (the "excessive" or unrepresented part of society) seeks to disrupt the order of domination and distribution of goods "naturalized" by police and legal institutions. In addition, the notion of "equality" operates as a game of contestation that constantly substitutes litigation for political action and community. This game, Ranciere maintains, operates by a primary logic of "misunderstanding". In turn, political philosophy has always tried to substitute the "politics of truth" for the politics of appearances. Disagreement investigates the various transformations of this regime of "truth" and their effects on practical politics. Ranciere then distinguishes what we mean by "democracy" from the practices of a consensual system in order to unravel the ramifications of the fashionable phrase "the end of politics". His conclusions will be of interest toreaders concerned with political questions from the broadest to the most specific and local. (shrink)

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

One of the most significant political philosophers of the twentieth century, Carl Schmitt is a deeply controversial figure who has been labeled both Nazi sympathizer and modern-day Thomas Hobbes. First published in 1938, The Leviathan in the State Theory of Thomas Hobbes used the Enlightenment philosopher’s enduring symbol of the protective Leviathan to address the nature of modern statehood. A work that predicted the demise of the Third Reich and that still holds relevance in today’s security-obsessed society, this volume will (...) be essential reading for students and scholars of political science. “Carl Schmitt is surely the most controversial German political and legal philosopher of this century. . . . We deal with Schmitt, against all odds, because history stubbornly persists in proving many of his tenets right.”— Perspectives on Political Science “[A] significant contribution. . . . The relation between Hobbes and Schmitt is one of the most important questions surrounding Schmitt: it includes a distinct, though occasionally vacillating, personal identification as well as an association of ideas.”— Telos. (shrink)

Thomas Hobbes took a new look at the ways in which society should function, and he ended up formulating the concept of political science. His crowning achievement, Leviathan, remains among the greatest works in the history of ideas. Written during a moment in English history when the political and social structures as well as methods of science were in flux and open to interpretation, Leviathan played an essential role in the development of the modern world. This edition of Hobbes' landmark (...) work is based on the original text. It incorporates the author's own corrections and retains the period spelling and punctuation, offering both flavorful authenticity and the utmost clarity of expression. (shrink)

To date, research examining the socio-spatial effects of smart city technologies have charted how they are reconfiguring the production of space, spatiality and mobility, and how urban space is governed, but have paid little attention to how the temporality of cities is being reshaped by systems and infrastructure that capture, process and act on real-time data. In this article, we map out the ways in which city-scale Internet of Things infrastructures, and their associated networks of sensors, meters, transponders, actuators and (...) algorithms, are used to measure, monitor and regulate the polymorphic temporal rhythms of urban life. Drawing on Lefebvre, and subsequent research, we employ rhythmanalysis in conjunction with Miyazaki’s notion of ‘algorhythm’ and nascent work on algorithmic governance, to develop a concept of ‘algorhythmic governance’. We then use this framing to make sense of two empirical case studies: a traffic management system and sound monitoring and modelling. Our analysis reveals: how smart city technologies computationally perform rhythmanalysis and undertake rhythm-making that intervenes in space-time processes; distinct forms of algorhythmic governance, varying on the basis of adaptiveness, immediacy of action, and whether humans are in-, on-, or, off-the-loop; and a number of factors that shape how algorhythmic governance works in practice. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, (...) history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

The ways citizen participation and democracy are changing are poorly understood due to the dominance of theories inherited from the eighteenth century. Democratic citizenship can be better understood if critical reflection is re-oriented around the games of concrete freedom here and now as recommended by Hannah Arendt, Ludwig Wittgenstein, Michel Foucault and Quentin Skinner.This orientation brings to light two distinctive types of citizen freedom in the present: diverse forms of citizen participation and diverse practices of governance in which citizens participate.

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms usually are (...) inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

It is argued that Pettit’s conception of “contestatory democracy” is superior to deliberative, direct and epistemic democracy. The strong and weak points of these conceptions are discussed drawing upon the work of a.o Bruce Bimber. It is further argued that ‘contestation’ and ‘information’ are highly relevant notions in thinking about, just, viable and sustainable design for E-democracy.

The paper focuses on concerns and legal challenges brought on by the use of algorithms. A particular class of algorithms that augment or replace analysis and decision-making by humans, i.e. data analytics and machine learning, is under scrutiny. Taking into account Balkin’s work on “the laws of an algorithmic society”, attention is drawn to obligations of transparency, matters of due process, and accountability. This US-centric analysis on drawbacks and loopholes of current legal systems is complemented with the analysis of norms (...) and principles of the EU data protection law, or “GDPR”. The aim is twofold. On the one hand, the intent is to shed light on some crucial differences between the US and EU law on the regulation of algorithmic operators, both public and private. Whereas, in the USA, scholars debate whether and to what extent new duties and responsibilities of algorithmic operators, e.g. information fiduciaries, have to amend the current framework of self-regulation and light government—as shown by the White House’s Office of Science and Technology Policy report from November 2016—in EU law much of the new duties and responsibilities of algorithmic operators have been passed upon them as data controllers. Whether such approaches will successfully tackle the normative challenges of the algorithmic society is, on the other hand, an open issue that will likely represent the main topic of debate over the next years. Disagreement may concern: the terms framing the legal question in e.g. statistical purposes of the data processing; how such terms are related to each other in legal reasoning ; and legal hard cases that will increasingly have to do with the principles that are at stake also but not only in data protection. By entrusting such legal hard cases to algorithms, or some sort of smart artificial agent, humans still bear full responsibility for the judgment of what is socially, ethically, and legally “plain” and “hard” in social affairs. The balance between delegation of decisions to algorithms and non-delegation will be the leitmotiv of the algorithmic society. Since the devil is in the detail, the current paper is devoted to some of them. (shrink)