By: John Priest,
on 10/17/2016
Initially, they had envisaged dozens of them: slim booklets that would handily summarize all of the important aspects of every parish in Ireland. It was the 1830s, and such a fantasy of comprehensive knowledge seemed within the grasp of the employees of the Ordnance Survey in Ireland.
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By: Eleanor Jackson,
on 9/24/2016
Opening the morning paper or browsing the web, routine actions for us all, rarely if ever shake our fundamental beliefs about the world. If we assume a naïve, reflective state of mind, however, reading newspapers and surfing the web offer us quite a different experience: they provide us with a glimpse into the kaleidoscopic nature of the modern era that can be quite irritating.
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By: Alice,
on 5/19/2014
Public international lawyers are forever in catch-up mode, or so it seems. The international legal appetite for ‘raw’ data of global life is seemingly inexhaustible and worry about the discipline lagging behind technology is perennial. There has, accordingly, been considerable energy devoted to ‘cybernating’ international law, in one way or another, or adapting the discipline to new possibilities posed by digital technology.
Much international legal writing concerned with computer and information technology (CIT) and global data flows has been concerned with developing law on these phenomena on the global plane. Scholars and practitioners of international law have, for instance, published important work on privacy and data protection and cyberwarfare.
Just as important, however, but receiving far less attention, are legal and equitable dimensions of the global data economy being envisioned by institutions such as the World Economic Forum. International law is often viewed, in this context, diminutively and technically: as a means of delivering on foregone conclusions and facilitating the realization of pre-agreed goals. Yet, as a recent paper in the London Review of International Law argued, there is much more at stake in the global laws surrounding data-gathering, data-mining and the monetization and use of datasets, than the technical assurance of frictionless interface and the protection of privacy. Whether with regard to global offshoring in the CIT industry, or global practices of data gathering and profit-seeking at the ‘bottom of the pyramid’, new modes of economic inequality are under construction, with law playing a crucial infrastructural role – a role which merits tougher questioning.
Another set of challenges for contemporary international lawyers arises from the turn to ‘big data’ — large-scale data mining and data analytics — for global governance. In the UN Global Pulse initiative, for example, the United Nations is mining digital data sources and using real-time data analytics to evaluate human wellbeing and vulnerability, and directing resources and policymaking attention accordingly. When states that are parties to the Convention on the International Trade in Endangered Species of Wild Fauna and Flora (CITES) gather to review the listing of animal and plant species for differing levels of treaty protection, they frequently act (in part) on the basis of species distribution modeling (SDM). This SDM will have been carried out by software implementing one among a number of possible presence/absence algorithms.
It is a routine preoccupation of international lawyers that global norms and public decision-making processes should be apparent to those whom they impact: transparency is today treated as a meta-principle of international legal order. Yet it is still unclear what ‘transparency’ could or should entail when decision-making processes in question are partially automated, use complex and dynamic algorithmic operations, and draw inputs from a range of public and private sources. In relation to SDM for CITES listing purposes, for instance, a recent report in Science suggested that the relevant software’s intricacies are not grasped by many scientist-modelers: there are ‘many in the SDM domain unable to interpret the original algorithms, much less understand how they were implemented in the distributed code’. One wonders what CITES decision-makers to whom SDM modeling outcomes are being delivered are making of this material, if many responsible for these models’ development are unable to interpret them satisfactorily. Another recent paper has drawn attention to the traps that big data analysis can present for policy-makers seeking up-to-the-minute insights on global populations’ health and wellbeing.
Public international lawyers will doubtless continue to pursue broad-ranging regulatory initiatives, regionally and globally, concerning cybercrime and data protection. Beyond these efforts, however, global policy-makers and international lawyers working in a far greater range of fields need to engage critically with the priorities, preferences and relations embedded in, or generated by, the software and hardware of global data gathering and analysis. Associations among co-patterners (or those correlated in some analytical pattern) may prove just as significant as those among co-citizens or fellow right-holders — if not more so — in the global operations of law.
Fleur Johns is a Professor in the Faculty of Law at UNSW Australia, Sydney and a contributor to the London Review of International Law, a new journal, published by Oxford University Press, which publishes highest-quality scholarship on international law from around the world; the first issue featuring Professor Johns’ article ‘The deluge’, discussing the significance of big data for public international law, is free to read online for a limited time.
The London Review of International Law publishes highest-quality scholarship on international law from around the world. Reflecting the pace and reach of developments in the field, the London Review seeks to capture the ways in which received ideas are being challenged and reshaped by new subject-matters, new participants, new conceptual apparatuses and new cross-disciplinary connections.
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Image: information weapon, keyboard grenade. Photo by -antonio-, iStockphoto.
The post We’re all data now appeared first on OUPblog.
By: ChloeF,
on 5/2/2014
Nowadays it appears impossible to open a newspaper or switch on the television without hearing about “big data”. Big data, it sometimes seems, will provide answers to all the world’s problems. Management consulting company McKinsey, for example, promises “a tremendous wave of innovation, productivity, and growth … all driven by big data”.
An alien observer visiting the Earth might think it represents a major scientific breakthrough. Google Trends shows references to the phrase bobbing along at about one per week until 2011, at which point there began a dramatic, steep, and almost linear increase in references to the phrase. It’s as if no one had thought of it until 2011. Which is odd because data mining, the technology of extracting valuable, useful, or interesting information from large data sets, has been around for some 20 years. And statistics, which lies at the heart of all of this, has been around as a formal discipline for a century or more.
Or perhaps it’s not so odd. If you look back to the beginning of data mining, you find a very similar media enthusiasm for the advances it was going to bring, the breakthroughs in understanding, the sudden discoveries, the deep insights. In fact it almost looks as if we have been here before. All of this leads one to suspect that there’s less to the big data enthusiasm than meets the eye. That it’s not so much a sudden change in our technical abilities as a sudden media recognition of what data scientists, and especially statisticians, are capable.
Of course, I’m not saying that the increasing size of data sets does not lead to promising new opportunities – though I would question whether it’s the “large” that really matters as much as the novelty of the data sets. The tremendous economic impact of GPS data (estimated to be $150-270bn per year), retail transaction data, or genomic and bioinformatics data arise not from the size of these data sets, but from the fact that they provide new kinds of information. And while it’s true that a massive mountain of data needed to be explored to detect the Higgs boson, the core aspect was the nature of the data rather than its amount.
Moreover, if I’m honest, I also have to admit that it’s not solely statistics which leads to the extraction of value from these massive data sets. Often it’s a combination of statistical inferential methods (e.g. determining an accurate geographical location from satellite signals) along with data manipulation algorithms for search, matching, sorting and so on. How these two aspects are balanced depends on the particular application. Locating a shop which stocks that out of print book is less of an inferential statistical problem and more of a search issue. Determining the riskiness of a company seeking a loan owes little to search but much to statistics.
Diagram of Total Information Awareness system designed by the Information Awareness Office
Some time after the phrase “data mining” hit the media, it suffered a backlash. Predictably enough, much of this was based around privacy concerns. A paradigmatic illustration was the Total Information Awareness project in the United States. Its basic aim was to search for suspicious behaviour patterns within vast amounts of personal data, to identify individuals likely to commit crimes, especially terrorist offences. It included data on web browsing, credit card transactions, driving licences, court records, passport details, and so on. After concerns were raised, it was suspended in 2003 (though it is claimed that the software continued to be used by various agencies). As will be evident from recent events, concerns about the security agencies monitoring of the public continues.
The key question is whether proponents of the huge potential of big data and its allied notion of open data are learning from the past. Recent media concern in the UK about merging of family doctor records with hospital records, leading to a six-month delay in the launch of the project, illustrates the danger. Properly informed debate about the promise and the risks is vital.
Technology is amoral — neither intrinsically moral nor immoral. Morality lies in the hands of those who wield it. This is as true of big data technology as it is of nuclear technology and biotechnology. It is abundantly clear — if only from the examples we have already seen — that massive data sets do hold substantial promise for enhancing the well-being of mankind, but we must be aware of the risks. A suitable balance must be struck.
It’s also important to note that the mere existence of huge data files is of itself of no benefit to anyone. For these data sets to be beneficial, it’s necessary to be able to use the data to build models, to estimate effect sizes, to determine if an observed effect should be regarded as mere chance variation, to be sure it’s not a data quality issue, and so on. That is, statistical skills are critical to making use of the big data resources. In just the same way that vast underground oil reserves were useless without the technology to turn them into motive power, so the vast collections of data are useless without the technology to analyse them. Or, as I sometimes put it, people don’t want data, what they want are answers. And statistics provides the tools for finding those answers.
David J. Hand is Professor of Statistics at Imperial College, London and author of Statistics: A Very Short Introduction
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Image credit: Diagram of Total Information Awareness system designed by the Information Awareness Office. Public domain via Wikimedia Commons
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