Data & Policy: A new venue to study and explore policy–data interaction

Abstract

Every era faces a unique set of challenges and dilemmas, but ours can credibly lay claim to some of the most complex and vexing that humankind may have ever confronted. From climate change to growing inequality to a rising tide of refugees: we face an intricate mesh of overlapping and interdependent difficulties, one that is pushing the limits of our existing policy and governance capabilities (Data for Policy, 2015; Meyer et al., 2017). What we require today are not so much (or not only) new solutions, but new ways for arriving at solutions (Susha et al., 2017). We need a twenty-first century paradigm of governance and policy making. Data, it is increasingly clear, will be central to this paradigm (Pentland, 2013; Kirkpatrick, 2012). Along with ever increasing computer storage and analytics capabilities, massive amounts of data generated from citizens, devices, and sensors provide decision makers the opportunity to monitor and manage public infrastructure in real time and predict future patterns when used responsibly (Engin and Treleaven, 2019; Janssen and Helbig, 2018). Data have the potential to transform every part of the policy-making life cycle-agenda setting and needs identification; the search for solutions; prototyping and implementation of solutions; enforcement; and evaluation (Janssen and Helbig, 2018). These are all critical, interlinked steps in addressing our societal challenges, and each of these needs a radical rethink. The idea that data could be a key differentiator is, of course, not a new one. Its potential has been evident for some time now (Wang et al., 2018), especially in the business world (Henke et al., 2016), but also in the policy community, where efforts to harness the power of information have yielded positive results in areas as disparate as gender equality (Fatehkia et al., 2018), improving urban traffic flows (Zhao et al., 2018), and enhancing regulatory compliance (Heat Seek, n.d.; Credit Suisse, n.d.). Successful data initiatives have been deployed by governments around the world in both developing and developed countries (Verhulst and Young, 2017a). Such initiatives have led to a growing recognition that data are and should increasingly be part of any effective governance toolkit. Despite such encouraging results, it is true that the policy world has generally lagged behind business in its use of data and data methods (Hou et al., 2011). Policy-data interactions or governance initiatives that use data have been the exception rather than the norm, isolated prototypes and trials rather than an indication of real, systemic change. There are various reasons for the generally slow uptake of data in policymaking, and several factors will have to change if the situation is to improve. In particular, advocates of more data (and we include ourselves among this number) will need to overcome the following obstacles and limitations: • Despite the number of successful prototypes and small-scale initiatives, policy makers' understanding of data's potential and its value proposition generall...

Artificial intelligence for political decision-making in the European Union: Effects on citizens’ perceptions of input, throughput, and output legitimacy

“…It is often presumed that the more data are analyzed, that is, preferably all available data as "N = all" trumps sampling, the greater is the potential to gain insights and receive the best result (Mayer-Schönberger and Cukier, 2013). Digital data are collected, accessed, and analyzed in real time, leading to substantial advances in analytics, modeling, and dynamic visualization (Craglia et al, 2018;Poel et al, 2018;Verhulst et al, 2019). This transformation of real-world phenomena into digital data is expected to provide a timely and undistorted view of societal mechanisms and institutions.…”

“…As a result, algorithmic or hybrid decision systems would probably marginalize the opportunities for citizen participation and thereby decrease input legitimacy. This reasoning, of course, applies primarily to those technologies that directly decide on policy, instead of being involved in less far-reaching stages of the policy cycle, such as agenda setting or policy evaluation (Verhulst et al, 2019). Furthermore, as Barocas and Selbst (2016) point out, minorities and other disadvantaged social groups are often underrepresented in existing digital data, making them vulnerable to be disregarded by ADM.…”

Governing-by-the-numbers/Statistical governance: Reflections on the future of official statistics in a digital and globalised society

“…More recently, the use of language has evolved to the extent that we are now talking about 'datadriven policy-making' [4] or more simply 'data-forpolicy (D4P)' [5]. The background to this is the enormously rapid increase in the amount of data that should be used -provided the appropriate technologies and methodologies are in place -to further improve public administration in general and political decisions in particular.…”

“…-On the other hand, after a few sentences of conversation, you find yourself confronted with one of the common jokes about statistics and statisticians: "I only believe in statistics that I doctored myself." 4 , "Lies, damned lies, and statistics" 5 or "Not everything that can be counted counts, and not everything that counts can be counted. 6 In contrast to the first group (the naïve positivists), a subtle mistrust and discomfort resonates, which feeds on professional ignorance and scepticism about the power of experts.…”

Data-driven anticipatory governance. Emerging scenarios indata for policypractices

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