In the 1930s, the Home Owners’ Loan Corporation (HOLC) graded the mortgage security of urban US neighborhoods. In doing so, the HOLC engaged in the practice, imbued with racism and xenophobia, of “redlining” neighborhoods deemed “hazardous” for lenders. Redlining has caused persistent social, political and economic problems for communities of color. Linkages between redlining and contemporary food access remain unexamined, even though food access is essential to well-being. To investigate this, we used a census tract-level measure of (...) low-income and low grocery store food access from the US Department of Agriculture Food Access Research Atlas, redlining data from Mapping Inequality Project, and demographic data from the American Community Survey. We employed generalized estimating equations with robust covariance estimates to analyze data pertaining to 10,459 census tracts in 202 US cities. Tracts that the HOLC graded as “C” (“decline in desirability”) and “D” (“hazardous”) had reduced contemporary food access compared to those graded “A” (“best”). Increases in contemporary census tract proportions of Black, Hispanic, or other racial/ethnic minority residents, as well as disabled residents, were associated with reduced food access. Increases in contemporary proportions of residents age 75 years and older or those without a car were associated with better food access. Tracts that underwent housing redevelopment since being graded had better food access, while those undergoing gentrification had reduced food access. Results suggest that issues of redlining, housing discrimination, racism, ableism, displacement, and food inaccessibility are deeply intertwined. (shrink)

The practice of Emergency Management in Michigan raises anew the question of whose knowledge matters to whom and for what reasons, against the background of what projects, challenges, and systemic imperatives. In this paper, I offer a historical overview of state intervention laws across the United States, focusing specifically on Michigan’s Emergency Manager laws. I draw on recent analyses of these laws to develop an account of a phenomenon that I call epistemic redlining, which, I suggest, is a form (...) of group-based credibility discounting not readily countenanced by existing, ‘culprit-based’ accounts of epistemic injustice. I argue that epistemic redlining plays a crucial role in ongoing projects of racialized subordination and dispossession in Michigan, and that such discounting tends to have structural causes that can be difficult to identify and uproot. Contrary to the general thrust of recent work on the topic, I argue that epistemic redlining ought to be understood as a form of epistemic injustice. (shrink)

Despite widespread recognition of an emergent politics of data in our midst, we strikingly lack a political theory of data. We readily acknowledge the presence of data across our political lives, but we often do not know how to conceptualize the politics of all those data points—the forms of power they constitute and the kinds of political subjects they implicate. Recent work in numerous academic disciplines is evidence of the first steps toward a political theory of data. This article maps (...) some limits of this emergent literature with an eye to enriching its theoretical range. The literature on data politics, both within political theory and elsewhere, has thus far focused almost exclusively on the algorithm. This article locates a further dimension of data politics in the work of formatting technology or, more simply, formats. Formats are simultaneously conceptual and technical in the ways they define what can even count as data, and by extension who can count as data and how they can count. A focus on formats is of theoretical value because it provides a bridge between work on the conceptual contours of categories and the technology-centric literature on algorithms that tends to ignore the more conceptual dimensions of data technology. The political insight enabled by format theory is shown in the context of an extended interrogation of the politics of racialized redlining. (shrink)

This paper develops a political economic explanation of the 2007–9 US subprime crisis which focuses on one of its central causes: the transformation of racial exclusion in US mortgage-markets. Until the early 1990s, racial minorities were systematically excluded from mortgage-finance due to bank-redlining and discrimination. But, then, racial exclusion in credit-markets was transformed: racial minorities were increasingly given access to housing-credit under terms far more adverse than were offered to non-minority borrowers. This paper shows that the emergence of the (...) subprime loan is linked, in turn, to the strategic transformation of banking in the 1980s, and to the unique global circumstances of the US macro-economy. Thus, subprime lending emerged from a combination of the long US history of racial exclusion in credit-markets, the crisis of US banking, and the position of the US within the global economy. From the viewpoint of the capitalist accumulation-process, these loans increased the depth of the financial expropriation of the working class by financial capital. The crisis in subprime lending then emerged when subprime loans with exploitative terms became more widespread and were made increasingly on an under-collateralised basis – that is, when housing-loans became not just extortionary but speculative. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...) as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

Data analytics, particularly the current rhetoric around “Big Data”, tend to be presented as new and innovative, emerging ahistorically to revolutionize modern life. In this article, we situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics. Spatial Big Data promises an epistemic break in marketing, a leap from targeting geodemographic areas to targeting individuals. Yet it inherits characteristics and problems from geodemographics, including a (...) justification through the market, and a process of commodification through the black-boxing of technology. As researchers develop sustained critiques of data analytics and its effects on everyday life, we must so with a grounding in the cultural and historical contexts from which data technologies emerged. This article and others develop a historically situated, critical approach to spatial Big Data. This history illustrates connections to the critical issues of surveillance, redlining, and the production of consumer subjects and geographies. The shared histories and structural logics of spatial Big Data and geodemographics create the space for a continued critique of data analyses’ role in society. (shrink)

This tribute compares Charity Scott to Fred Rogers, highlighting how Charity nurtured health law colleagues’ unique gifts and built community. Continuing the neighborhood theme, it highlights encouraging developments relating to health, housing, and place: Medicaid housing supports and potential reparations for redlining-related health inequities.

There is increasing interest in the EU about the central place of eIdentity (eID) in people’s lives. eID is increasingly seen as a bridge between the commercial viability of models based on large-scale provision of e-services and users’ need for privacy and security in online transactions. This paper examines technological, social and legal developments in the field of eID and asks whether there is the need for a new regulatory framework that both preserves users’ identity and enables the provision of (...) advanced services. Firstly, the paper interprets recent market moves in the eID field as a response to a rising regulatory tide. Secondly, it examines some of the challenges arising from Web2.0, and four emerging socio-legal issues associated with eID—behavioural profiling, social engineering, redlining and other unsocial practices. Thirdly, the paper examines the capacity of the current regulatory framework to absorb this turbulence, and finds it wanting. Finally, it advances a novel model of eID regulation that may help regulators create an identity-preserving, transaction-friendly eID environment. (shrink)