Lyria Bennett Moses

Is Big Data challenging criminology

The advent of ‘Big Data’ and machine learning algorithms is predicted to transform how we work and think. Specifically, it is said that the capacity of Big Data analytics to move from sampling to census, its ability to deal with messy data and the demonstrated utility of moving from causality to correlation have fundamentally changed the practice of social sciences. Some have even predicted the end of theory—where the question why is replaced by what—and an enduring challenge to disciplinary expertise. This article critically reviews the available literature against such claims and draws on the example of predictive policing to discuss the likely impact of Big Data analytics on criminological research and policy.

Algorithmic prediction in policing: assumptions, evaluation, and accountability

ABSTRACT The goal of predictive policing is to forecast where and when crimes will take place in the future. The idea has captured the imagination of law enforcement agencies around the world. Many agencies are purchasing software tools with the goal of reducing crime by mapping the likely locations of future crime to guide the deployment of police resources. Yet the claims and promises of predictive policing have not been subject to critical examination. This paper provides a review of the theories, techniques, and assumptions embedded in various predictive tools and highlights three key issues about the use of algorithmic prediction. Assumptions: The algorithms used to gain predictive insights build on assumptions about accuracy, continuity, the irrelevance of omitted variables, and the primary importance of particular information (such as location) over others. In making decisions based on these algorithms, police are also directed towards particular kinds of decisions and responses to the exclusion of others. Evaluation: Media coverage of these technologies implies that they are successful in reducing crime. However, these claims are not necessarily based on independent, peer reviewed evaluations. While some evaluations have been conducted, additional rigorous and independent evaluations are needed to understand more fully the effect of predictive policing programmes. Accountability: The use of predictive software can undermine the ability for individual officers or law enforcement agencies to give an account of their decisions in important ways. The paper explores how this accountability gap might be reduced.

Agents of change: How the law 'Copes' with technological change

The difficulty of adapting legal frameworks to changing circumstances is often represented by images of ‘law’ losing a race. Such visions are commonly raised in scholarship concerned with particular problems that arise in applying existing legal rules to new situations, particularly situations involving new technologies. The dilemmas encountered in adapting legal frameworks to technological change rarely persist indefinitely, however. While no institution or methodology is exclusively concerned with changing the law in response to technological change, parliamentary committees, government departments, royal commissions, law reform organisations, technology assessment agencies, ethics bodies, courts, the Productivity Commission and an array of individuals and ad hoc bodies have all been sources of adjustment at various points in history. The diverse array of organisations represents a multiplicity of disciplinary perspectives and evolving methodologies. Each one focuses on part of the story of the mutual adjustment between technology, society and law. This article represents an initial attempt to survey the landscape in order to understand better how Australia has dealt with both technological ‘crises’ and the more mundane process of ensuring that legal rules operate sensibly and predictably in an evolving technological environment. From this, the article will explore briefly the gaps in Australia’s current mechanisms for ensuring law ‘keeps up’ with technology.

How to Think About Law, Regulation and Technology: Problems with 'Technology' as a Regulatory Target

This article explores the ways in which scholars exploring issues around law, regulation and technology can engage in meaningful conversations that cross doctrinal and technological categories. It argues that discussions around “technology regulation” add little to theories of regulation generally and that, to the extent “technology” deserves special consideration, it is because of the types of issues that arise as technology changes. The real issue is thus exploring how law and regulation can remain connected with changing socio-technical practices, whether or not technology is the regulatory target.

Regulating Beyond Nanotechnology

When a new technology generates a need for legal change, there are questions about the form that change should take. The fact that legal problems accompany the introduction of nanotechnology does not mean that all related legal amendments are optimally limited to that context. This article considers some of the disadvantages of creating nano-specific sui generis rules to cope with legal and regulatory issues arising out of nanotechnology.

Sui Generis Rules

Technological change is often accompanied by legal problems – calls for regulation, legal uncertainty in the face of new situations, poor fit between existing legal frameworks and the new technology, and obsolescent law. In the face of these problems, there is often pressure for “special” or sui generis rules to deal with new situations brought about by the new technology. On the other side, “technology neutral” legislation is widely perceived as desirable. This chapter discussed the benefits and disadvantages of creating sui generis rules as a method for adapting the law to new technologies.

The Productivity Commission: A Different Engine for Law Reform?

This empirical study analyses the extent to which the Productivity Commission (Commission) relies on different types of evidence in formulating recommendations in a sample of reports. It goes deeper than traditional citation analysis; rather than classifying and counting all material cited in each sample report, it includes only citations that influenced the final recommendations. The findings, which run counter to the rhetoric employed in relation to the Commissions work, reveal the extent to which the Commission relies on non-quantitative forms of evidence, including bare assertions, personal experience, and logical and legal arguments, particularly in reports addressing broader questions of social policy. It concludes with a discussion of the significance of these findings, linking them to Graycars critique of law reform commissions. As such, it provides a more accurate, but still preliminary, basis for understanding the Commissions methods than that otherwise appearing in the discourse surrounding the Commissions work.

Academic Metrics and Positioning Strategies

Abstract Since the 1980s, higher education institutions in many developed Western countries have been facing competition for resources, have undergone economic rationalisation, adopted a New Public Management style of performance management and aspired to meet global standards of quality. This chapter explores the self-tracking practices of academic institutions and workers as they negotiate a field that has moved away from a quality evaluation system based primarily on social reputation towards one based increasingly on quantified outcome indicators. Universities typically measure research performance not only in terms of quantity of outputs but also the ‘attention capital’ they receive, for example, the number of citations or awards and prizes. These metrics and the emphasis on attention capital generally encourage a culture of competition rather than collaboration, while promoting the ‘celebrification’ of academic life. We argue that this trend has been intensified by technologies that gamify research achievements, continuously update citation and ‘read’ counts, and promote networked reputation. Under these conditions, academic institutions and workers have attempted to pursue a variety of positioning strategies that represent different degrees of conformity, resistance and compromise to the power of metrics.

Data associations in global law and policy

Social phenomena—or the condition of society—may be ‘‘seized indirectly when there is a slight change in one older association mutating into a slightly newer or different one’’ (Latour, 2005: 36). The aim of this special issue is to trace mutations underway in those associations rendered or experienced in data and to probe some patterns that these changing ties draw. In particular, contributors to this issue reflect upon associations traceable in data that are of a juridical nature (or could be so understood), or that have salience for legal institutions and norms. This is something other than inviting consideration of ‘‘problems’’ that technology makes for law. It is something other, too, than thinking about whether law does or does not determine or reflect socio-technical practice, or vice versa, and how such law-technology correspondence might ‘‘properly’’ be maintained. Instead, contributors engage here in a collective experiment of envisioning data as vectors of lawful relations on the global plane, and at other scales. This is unfinished business for Big Data & Society. In this journal’s opening issue, Rob Kitchin argued that ‘‘the development of digital humanities and computational social sciences. . . propose radically different ways to make sense of culture, history, economy and society’’ (Kitchin, 2014: 1). But what ‘‘sense’’ could ‘‘Big Data empiricism,’’ as Kitchin described it, make in, of and for global law and policy? This is among the questions that the contributors to this special issue take up. Neither digital technology nor law is pivotal to this inquiry, so much as their irrepressible leaking and morphing into would-be or could-be versions of the other. As paradigmatic a shift as the turn to epistemologies of Big Data might seem, making connections between these emergent epistemologies and ‘‘older association[s],’’ in Latour’s words, is also an important task of this collection. Sheila Jasanoff traces, for instance, the history of the production of ‘‘a panoptic viewpoint from which the entire diversity of human experience can be seen, catalogued, aggregated, and mined’’ from the mid-20th-century, especially in the emergence of the ‘‘global environment’’ as an ‘‘actionable object for law and policy.’’ Naveen Thayyil likewise draws an analogy between change in weather and climatological studies from the 1960s onwards (from instrument reading techniques to computer modeling) and parallel shifts in approaches to risk regulation (from conventional risk assessment to precautionary approaches, the latter increasingly advanced through ‘‘Big Data’’ automation). Ben Hurlbut similarly connects ‘‘scientifically authorized imaginations of future risk’’ on the global plane to earlier incarnations of the ‘‘republic of science’’ assembled around pandemic risk since the 19th-century. Other contributions to this volume re-frame contemporary phenomena by reference to associations of more recent provenance: Sarah Logan analyses ‘‘post 9-11 mass surveillance’’ and the ‘‘anxious information state’’ it enshrines. Likewise, Gavin Smith, Kath Albury, Jean Burgess, Ben Light, Kane Race, Rowan Wilken, and Daniel Joyce focus on ‘‘data cultures’’ ascendant during the past decade and the legal, social, and political conflicts and connections that surface amid them. The protagonists and environs of the stories told in these pages vary greatly. Not all are of a kind that one

No More Excuses: Automated Synthesis of Practical and Verifiable Vote-Counting Programs for Complex Voting Schemes.

We argue that electronic vote-counting software can engender broad-based public trust in elections to public office only if they are formally verified against their legal definition and only if they can produce an easily verifiable certificate for the correctness of the count. We then show that both are achievable for the Schulze method of vote-counting, even when the election involves millions of ballots. We argue that our methodology is applicable to any vote-counting scheme that is rigorously specified. Consequently, the current practice of using unverified and unverifiable vote counting software for elections to public office is untenable. In particular, proprietary closed source vote-counting software is simply inexcusable.