Linnet Taylor

Critical Data Studies: A Dialog on Data and Space

In light of recent technological innovations and discourses around data and algorithmic analytics, scholars of many stripes are attempting to develop critical agendas and responses to these developments (boyd and Crawford 2012). In this mutual interview, three scholars discuss the stakes, ideas, responsibilities, and possibilities of critical data studies. The resulting dialog seeks to explore what kinds of critical approaches to these topics, in theory and practice, could open and make available such approaches to a broader audience.

Emerging practices and perspectives on Big Data analysis in economics: Bigger and better or more of the same?

Although the terminology of Big Data has so far gained little traction in economics, the availability of unprecedentedly rich datasets and the need for new approaches – both epistemological and computational – to deal with them is an emerging issue for the discipline. Using interviews conducted with a cross-section of economists, this paper examines perspectives on Big Data across the discipline, the new types of data being used by researchers on economic issues, and the range of responses to this opportunity amongst economists. First, we outline the areas in which it is being used, including the prediction and ‘nowcasting’ of economic trends; mapping and predicting influence in the context of marketing; and acting as a cheaper or more accurate substitute for existing types of data such as censuses or labour market data. We then analyse the broader current and potential contributions of Big Data to economics, such as the ways in which econometric methodology is being used to shed light on questions beyond economics, how Big Data is improving or changing economic models, and the kinds of collaborations arising around Big Data between economists and other disciplines.

No place to hide? The ethics and analytics of tracking mobility using mobile phone data

This paper examines the ethical and methodological problems with tracking human mobility using data from mobile phones, focusing on research involving low- and middle-income countries. Such datasets are becoming accessible to an increasingly broad community of researchers and data scientists, with a variety of analytical and policy uses proposed. This paper provides an overview of the state of the art in this area of research, then sets out a new analytical framework for such data sources that focuses on three pressing issues: first, interpretation and disciplinary bias; second, the potential risks to data subjects in low- and middle-income countries and possible ethical responses; and third, the likelihood of ‘function creep’ from benign to less benign uses. Using the case study of a data science challenge involving West African mobile phone data, I argue that human mobility is becoming legible in new, more detailed ways, and that this carries with it the dual risk of rendering certain groups invisible and of misinterpreting what is visible. Thus, this emerging ability to track movement in real time offers both the possibility of improved responses to conflict and forced migration, but also unprecedented power to surveil and control unwanted population movement.

Big data and Wikipedia research: social science knowledge across disciplinary divides

This paper examines research about Wikipedia that has been undertaken using big data approaches. The aim is to gauge the coherence as against the disparateness of studies from different disciplines, how these studies relate to each other, and to research about Wikipedia and new social media in general. The paper is partly based on interviews with big data researchers, and discusses a number of themes and implications of Wikipedia research, including about the workings of online collaboration, the way that contributions mirror (or not) aspects of real-world geographies, and how contributions can be used to predict offline social and economic trends. Among the findings is that in some areas of research, studies build on and extend each others results. However, most of the studies stay within disciplinary silos and could be better integrated with other research on Wikipedia and with research about new media. Wikipedia is among few sources in big data research where the data are openly available, unlike many studies where data are proprietary. Thus, it has lent itself to a burgeoning and promising body of research. The paper concludes that in order to fulfil this promise, this research must pay more attention to theories and research from other disciplines, and also go beyond questions based narrowly on the availability of data and towards a more powerful analytical grasp of the phenomenon being investigated.

Big data and positive change in the developing world

This paper is the product of a workshop that brought together practitioners, researchers, and data experts to discuss how big data is becoming a resource for positive social change in low- and middle-income countries (LMICs). We include in our definition of big data sources such as social media data, mobile phone use records, digitally mediated transactions, online news media sources, and administrative records. We argue that there are four main areas where big data has potential for promoting positive social change: advocacy; analysis and prediction; facilitating information exchange; and promoting accountability and transparency. These areas all have particular challenges and possibilities, but there are also issues shared across them, such as open data and privacy concerns. Big data is shaping up to be one of the key battlefields of our time, and the paper argues that this is therefore an opportune moment for civil society groups in particular to become a larger part of the conversation about the use of big data, since questions about the asymmetries of power involved are especially urgent in these uses in LMICs. Civil society groups are also currently underrepresented in debates about privacy and the rights of technology users, which are dominated by corporations, governments and nongovernmental organizations in the Global North. We conclude by offering some lessons drawn from a number of case studies that represent the current state-of-the-art.

What is data justice? The case for connecting digital rights and freedoms globally:

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly connected to a social justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: (in)visibility, (dis)engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract.

Big data and urban governance

This chapter examines the ways in which big data is involved in the rise of smart cities. Mobile phones, sensors and online applications produce streams of data which are used to regulate and plan the city, often in real time, but which presents challenges as to how the city’s functions are seen and interpreted. Using a socio-technical approach, we offer a critical evaluation of the types of data being used in urban governance and their advantages and drawbacks in comparison to previous information systems. Using examples from New York and Abidjan, Cote d’Ivoire, we demonstrate how big data can both illuminate and obscure our understanding of urban development. We outline methodological considerations for the use of such data, offering conclusions towards the development of a critical urban data science.

The ethics of big data as a public good: which public? Whose good?

International development and humanitarian organisations are increasingly calling for digital data to be treated as a public good because of its value in supplementing scarce national statistics and informing interventions, including in emergencies. In response to this claim a ‘responsible data’ movement has evolved to discuss guidelines and frameworks that will establish ethical principles for data sharing. However, this movement is not gaining traction with those who hold the highest-value data, particularly mobile network operators who are proving reluctant to make data collected in low- and middle-income countries accessible through intermediaries. This paper evaluates how the argument for ‘data as a public good’ fits with the corporate reality of big data, exploring existing models for data sharing. I draw on the idea of corporate data as an ecosystem involving often conflicting rights, duties and claims, in comparison to the utilitarian claim that data’s humanitarian value makes it imperative to share them. I assess the power dynamics implied by the idea of data as a public good, and how differing incentives lead actors to adopt particular ethical positions with regard to the use of data.