The ‘golden view’: data-driven governance in the scoring society' by
Lina Dencik, Joanna Redden, Arne Hintz and Harry Warne in (2019) 8(2)
Internet Policy Review comments
Drawing on the first comprehensive investigation into the uses of data analytics in UK public services, this article outlines developments and practices surrounding the upsurge in data-driven forms of what we term ‘citizen scoring’. This refers to the use of data analytics in government for the purposes of categorisation, assessment and prediction at both individual and population level. Combining Freedom of Information requests and semi-structured interviews with public sector workers and civil society organisations, we detail the practices surrounding these developments and the nature of concerns expressed by different stakeholder groups as a way to elicit the heterogeneity, tensions and negotiations that shape the contemporary landscape of data-driven governance. Described by practitioners as a way to achieve a ‘golden view’ of populations, we argue that data systems need to be situated in this context in order to understand the wider politics of such a ‘view’ and the implications this has for state-citizen relations in the scoring society.
The authors argue
Questions about how data is generated, collected and used have taken hold of public imagination in recent years, not least in relation to government. While the collection of data about populations has always been central to practices of governance, the digital era has placed increased emphasis on the politics of data in state-citizen relations and contemporary power dynamics. In part a continuation of long-standing processes of bureaucratisation, the turn to data-centric practices in government across Western democracies emerges out of a significant moment in the securitisation of politics, the shrinking of the public sector, and the rise of corporate power. In the case of the United Kingdom, this is particularly brought to bear through an on-going austerity agenda since the financial crisis of 2008. Data analytics, in this context, is increasingly viewed and sold as providing a means to more efficiently target and deliver public services and to better understand social problems (Beer, 2018).
As government has entered into this space, adopting the processes, logics and technologies of the private sector, this raises major questions about the nature of contemporary governance and the socio-technical shaping of citizenship. Of particular concern is how new and often obscure systems of categorisation, risk assessment, social sorting and prediction may influence funding and resource decisions, access to services, intensify surveillance and determine citizen status or worth. The proliferation of data sharing arrangements among government agencies is raising concerns about who is accessing citizen data, the potential for highly personal profiling, function creep and misuse. At the same time, the black boxed nature of big data processes, the dominant myths about data systems as objective and neutral, as well as the inability of most to understand these processes makes interrogating government data analytics systems difficult for researchers and near impossible for citizens without adequate resources (Pasquale, 2015; O’Neil, 2016; Kitchin, 2017).
Moreover, the empirical underpinning for a more thorough understanding of these dynamics remains obscure as the implementation of data analytics in public services is only emerging. In this article we therefore contribute with an overview of developments of data analytics in public services in the particular case of the UK. Drawing on research carried out for the one-year project ‘Data Scores as Governance’, the article provides the first integrated analysis of the use of such systems in the UK and of the often polarised views and approaches among stakeholders. In mapping this emerging field, we explore the way these data systems are situated and used in practice, engaging with the myriad negotiations and challenges that emerge in this context.
The article identifies an upsurge in data-driven forms of what we term ‘citizen scoring’ - the use of data analytics in government for the purposes of categorisation, assessment and prediction at both individual and population level. It demonstrates citizen scoring as a situated practice that emerges from an amalgamation of actors, imaginaries and political and economic forces that together shape and contest what was described in our research as a desired ‘golden view’ of citizens. The article thus highlights the heterogeneity of data practices, and points to the need for a nuanced understanding of the contingency of data systems on significant contextual factors that moves us beyond an engagement with the technologies themselves, towards a wider politics of their development, deployment, implementation and use as part of understanding the nature of citizenship in an emerging ‘scoring society’.
'Social Credit Systems in China' by Chuncheng Liu
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In 2014, the State Council of the People’s Republic of China (State Council) issued a blueprint, aimed to build a Social Credit System (SCS) to solve various social problems in the society by evaluating, classifying, awarding and punishing people based on their trustworthiness. Since 2014, various local experiments had been enacted in many Chinese cities with various innovating use of digital technologies.
Based on the data I have collected from governmental policies (both central and local) and newspapers articles, this paper comprehensively explores and articulates the complicated multiplicity character of current Chinese SCSs and examine relationships among them. There are two broad fields of SCS experiments now: governmental and commercial. Commercial SCSs for institutions were developed in the 1990s, while commercial SCSs for individual, such as sesame score, started only after 2015. Both of these commercial credit systems are supervised by the People’s Bank of China (PBOC), China’s central bank. For governmental SCSs, we can further divide two levels of SCSs: national and municipal. Governmental national SCSs have two systems led by different governmental agencies. The first one is a financial credit system led by PBOC. The second is a blacklist-based system led by the Supreme People’s Court (SPC) and the National Development and Reform Commission (NDRC), a macroeconomic management governmental agency under the State Council. On the municipal level, different municipal governments are experimenting their own SCSs and propose different conceptualizations of what “(dis)credit” and “(mis)trustworthiness” are.
After showing the multiplicity of Chinese SCSs, I will historize current SCSs and shows that many elements and assumptions of SCSs after 2014 can be traced back to a broader People’s Republic of China’s (PRC) political history. At last, I will propose an alternative theoretical framework to understand Chinese SCSs beyond a simple repressive and direct political project, but a symbolic system with performative power.
