The popular science of phrenology is known for its preoccupation with geniuses and criminals, but this article shows that phrenologists also introduced ideas about the ‘average’ person. Popular phrenologists in the US and the UK examined the heads of their clients to give an indication of their character. Based on the publications of phrenologists and on a large collection of standardized charts with clients’ scores, this article analyses their definition of what they considered to be the ‘average’. It can be (...) concluded that phrenologists were some of the first to teach individuals to see their identity in relation to an imagined statistical community. (shrink)

In actuarial parlance, the price of an insurance policy is considered fair if customers bearing the same risk are charged the same price. The estimate of this fair amount hinges on the expected value obtained by weighting the different claims by their probability. We argue that, historically, this concept of actuarial fairness originates in an Aristotelian principle of justice in exchange (equality in risk). We will examine how this principle was formalized in the 16th century and shaped in life insurance (...) during the following two hundred years, in two different interpretations. The Domatian account of actuarial fairness relied on subjective uncertainty: An agreement on risk was fair if both parties were equally ignorant about the chances of an uncertain event. The objectivist version grounded any agreement on an objective risk estimate drawn from a mortality table. We will show how the objectivist approach collapsed in the market for life annuities during the 18th century, leaving open the question of why we still speak of actuarial fairness as if it were an objective expected value. (shrink)

AI-driven decisions can draw data from virtually any area of your life to make a decision about virtually any other area of your life. That creates fairness issues. Effective regulation to ensure fairness requires that AI systems be transparent. That is, regulators must have sufficient access to the factors that explain and justify the decisions. One approach to transparency is to require that systems be explainable, as that concept is understood in computer science. A system is explainable if one can (...) provide a human-understandable explanation of why it makes any particular prediction. Explainability should not be equated with transparency. To address transparency and characterize its relation to explainability, we define transparency for a regulatory purpose. A system is transparent for a regulatory purpose (r-transparent) when and only when regulators have an explanation, adequate for that purpose, of why it yields the predictions it does. Explainability remains relevant to transparency but turns out to be neither necessary nor sufficient for it. The concepts of explainability and r-transparency combine to yield four possibilities: explainable and either r-transparent or not; and not explainable and either not r-transparent or r-transparent. Combining r-transparency with ideas from the Harvard computer scientist Cynthia Dwork, we propose four requirements on AI systems. (shrink)

Amidst the climate of crisis surrounding the rise in opioid-related overdose in the USA, early in 2019, Google and Deloitte launched ‘Opioid360’. Here came a platform combining browser histories, credit, insurance, social media, and traditional survey data to sell the service of risk calculation in population health. Opioid360's approach to automating risk calculation not only promised to identify persons ‘at risk’ of opioid dependence, but also paved the way for broader applications anticipating common chronic diseases and coordinating logistical operations involved (...) in pandemic response. Beginning with this experimental platform, this paper develops an analysis of the Big Data mode of risk calculation - an epistemological and political shift that involves technology companies, investors, insurers, governments, and public health institutions. The analysis focuses on the re-emergence of ‘social determinants of health’ in the rhetoric accompanying novel analytic platforms that estimate, calculate, and compute individual health risks. While the treatment of SDOH has always been a site of political contestation within the discipline of public health, powerful interests are crystallising around the concept and instrumentalising it in platforms that sell algorithmic prediction. Silicon Valley's breed of asset-oriented technoscience appears not only to be amplifying the behaviouralist elements of public health. Among the stakes of the Big Data mode is the paradoxical retreat from changing social conditions that contribute to the prevalence of health and illness in populations; and instead, the promotion of an apparatus for pricing and exchanging individual risk or excluding from services those who bear risk most acutely. (shrink)

While many forms of data-driven surveillance are now a ‘fact’ of contemporary life amidst datafication, obtaining concrete knowledge of how different institutions exploit data presents an ongoing challenge, requiring the expertise and power to untangle increasingly complex and opaque technological and institutional arrangements. The how and why of potential surveillance are thus wrapped in a form of continuously produced uncertainty. How then, do affected groups and individuals determine how to counter the threats and harms of surveillance? Responding to an interdisciplinary (...) concern with agency amidst datafication, this article explores what I term ‘anticipatory data practices’ – future-oriented practices which provide a concrete anchor and a heuristic for action amidst the persistent uncertainties of life with data. This article traces how anticipatory data practices have emerged within civil society practices concerned with countering the harms of surveillance and data exploitation. The mixed-method empirical analysis of this article draws from 50 interviews with digital security educators and technology developers; participant observation at 12 civil society events between 2016 and 2019 and the textual analysis of 100 security manuals produced by NGOs and grassroots groups. (shrink)

Predictive uses of data are becoming widespread in institutional settings as actors seek to anticipate people and their activities. Predictive modeling is increasingly the subject of scholarly and public criticism. Less common, however, is scrutiny directed at the data that inform predictive models beyond concerns about homogenous training data or general epistemological critiques of data. In this paper, I draw from a qualitative case study set in higher education in the United States to investigate the making of data. Data analytics (...) projects at universities have become more pervasive and intensive to better understand and anticipate undergraduate student bodies. Drawing from 12 months of ethnographic research at a large public university, I analyze the ways data personnel at the institution—data scientists, administrators, and programmers—sort student data into “attributes” and “behaviors,” where “attributes” are demographic data that students “can’t change.” “Behaviors,” in contrast, are data defined as reflective of what students can choose: attending and paying attention in class, studying on campus, among other data which personnel categorize as what students have control over. This discursive split enables the institution nudge students to make responsible choices according to behavior data that correlate with success in the predictive model. In discussing how personnel type, sort, stabilize, and nudge on behavior data, this paper examines the contingencies of data making processes and implications for the application of student data. (shrink)

Spatial Big Data—be this natively geocoded content, geographical metadata, or data that itself refers to spaces and places—has become a pervasive presence in the spaces and practices of everyday life. Beyond preoccupations with “the geotag” and with mapping geocoded social media content, this special theme explores what it means to encounter and experience spatial Big Data as a quotidian phenomenon that is both spatial, characterized by and enacting of material spatialities, and spatializing, configuring relations between subjects, objects, and spaces in (...) new and unprecedented ways. (shrink)

This article examines life assurance and the politics of ‘big data’ in mid-19th-century Britain. The datasets generated by life assurance companies were vast archives of information about human longevity. Actuaries distilled these archives into mortality tables – immensely valuable tools for predicting mortality and so pricing risk. The status of the mortality table was ambiguous, being both a public and a private object: often computed from company records they could also be extrapolated from public projects such as the census, or (...) clerical records. Life assurance more generally straddled the line between private enterprise and collective endeavour, though its advocates stressed the public interest in its success. Reforming actuaries such as Thomas Rowe Edmonds wanted the data on which mortality tables were based to be made publicly available, but faced resistance. Such resistance undermined insurers’ claims to be scientific in spirit and hindered Edmonds’s personal quest for a law of mortality. Edmonds pushed instead for an open actuarial science alongside fellow travellers at the Statistical Society of London, which was populated by statisticians such as William Farr as well as by radical mathematicians such as Charles Babbage. The article explores Babbage’s little-known foray into the world of insurance, both as a budding actuary but also as a fierce critic of the industry. These debates over the construction, ownership, and accessibility of insurance datasets show that concern about the politics of big data did not begin in the 21st century. (shrink)