Paper by Tarun Ramadorai, Antoine Uettwiller
Platform Surveillance
Editorial by David Murakami Wood and Torin Monahan of Special Issue of Surveillance and Society: “This editorial introduces this special responsive issue on “platform surveillance.” We develop the term platform surveillance to account for the manifold and often insidious ways that digital platforms fundamentally transform social practices and relations, recasting them as surveillant exchanges whose coordination must be technologically mediated and therefore made exploitable as data. In the process, digital platforms become dominant social structures in their own right, subordinating other institutions, conjuring or sedimenting social divisions and inequalities, and setting the terms upon which individuals, organizations, and governments interact.
Emergent forms of platform capitalism portend new governmentalities, as they gradually draw existing institutions into alignment or harmonization with the logics of platform surveillance while also engendering subjectivities (e.g., the gig-economy worker) that support those logics. Because surveillance is essential to the operations of digital platforms, because it structures the forms of governance and capital that emerge, the field of surveillance studies is uniquely positioned to investigate and theorize
Responsible Data Governance of Neuroscience Big Data
Neuroscience innovation relies upon neuroinformatics, large-scale data collection
Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection
Responsible data sharing in international health research: a systematic review of principles and norms
Paper by Shona Kalkman, Menno Mostert, Christoph Gerlinger, Johannes J. M. van Delden
We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement. However, at the level of principles and
While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to
Social capital predicts corruption risk in towns
Paper by Johannes Wachs, Taha Yasseri, Balázs Lengyel and János Kertész: “Corruption is a social plague: gains accrue to small groups, while its costs are borne by everyone. Significant variation in its level between and within countries suggests a relationship between social structure and the prevalence of corruption, yet, large-scale empirical studies thereof have been missing due to lack of data. In this paper, we relate the structural characteristics of social capital of settlements with corruption in their local governments. Using datasets from Hungary, we quantify corruption risk by suppressed competition and lack of transparency in the settlement’s awarded public contracts. We characterize social capital using social network data from a popular online platform. Controlling for social, economic and political factors, we find that settlements with fragmented social networks, indicating an excess of bonding social capital has higher corruption risk, and settlements with more diverse external connectivity, suggesting a surplus of bridging social capital is less exposed to corruption. We interpret fragmentation as fostering in-group
Data-driven models of governance across borders
Introduction to Special Issue of FirstMonday, edited by Payal Arora and Hallam Stevens: “This special issue looks closely at contemporary data systems in diverse global contexts and through this set of papers, highlights the struggles we face as we negotiate efficiency and innovation with universal human rights and social inclusion. The studies presented in these essays are situated in diverse models of policy-making, governance, and/or activism across borders. Attention to big data governance in western contexts has tended to highlight how data increases state and corporate surveillance of citizens, affecting rights to privacy. By moving beyond Euro-American borders — to places such as Africa, India, China, and Singapore — we show here how data regimes are motivated and understood on very different terms
To establish a kind of baseline, the special issue opens by considering attitudes toward big data in Europe. René König’s essay examines the role of “citizen conferences” in understanding the public’s view of big data in Germany. These “participatory technology assessments” demonstrated that citizens were concerned about the control of big data (should it be under the control of the government or individuals?), about the need for more education about big data technologies, and the need for more government regulation. Participants expressed, in many ways, traditional liberal democratic views and concerns about these technologies centered on individual rights, individual responsibilities, and education. Their proposed solutions too — more education and more government regulation — fit squarely within western liberal democratic traditions.
In contrast to this, Payal Arora’s essay draws us immediately into the vastly different contexts of data governance in India and China. India’s Aadhaar biometric identification system, through tracking its citizens with iris scanning and other measures, promises to root out corruption and provide social services to those most in need. Likewise, China’s emerging “social credit system,” while having immense potential for increasing citizen surveillance, offers ways of increasing social trust and fostering more responsible social behavior online and offline. Although the potential for authoritarian abuses of both systems is high, Arora focuses on how these technologies are locally understood and lived on an everyday basis, which spans from empowering to oppressing their people. From this perspective, the technologies offer modes of “disrupt[ing] systems of inequality and oppression” that should open up new conversations about what democratic participation can and should look like in China and India.
If China and India offer contrasting non-democratic and democratic cases, we turn next to a context that is neither completely western nor completely non-western, neither completely democratic nor completely liberal. Hallam Stevens’ account of government data in Singapore suggests the very different role that data can play in this unique political and social context. Although the island state’s data.gov.sg participates in global discourses of sharing, “open data,” and transparency, much of the data made available by the government is oriented towards the solution of particular economic and social problems. Ultimately, the ways in which data are presented may contribute to entrenching — rather than undermining or transforming — existing forms of governance. The account of data and its meanings that is offered here once again challenges the notion that such data systems can or should be understood in the same ways that similar systems have been understood in the western world.
If systems such as Aadhaar, “social credit,” and data.gov.sg profess to make citizens and governments more visible and legible, Rolien Hoyngexamines what may remain invisible even within highly pervasive data-driven systems. In the world of e-waste, data-driven modes of surveillance and logistics are critical for recycling. But many blind spots remain. Hoyng’s account reminds us that despite the often-supposed all-seeing-ness of big data, we should remain attentive to what escapes the data’s gaze. Here, in midst of datafication, we find “invisibility, uncertainty, and, therewith, uncontrollability.” This points also to the gap between the fantasies of how data-driven systems are supposed to work, and their realization in the world. Such interstices allow individuals — those working with e-waste in Shenzhen or Africa, for example — to find and leverage hidden opportunities. From this perspective, the “blind spots of big data” take on a very different significance.
Big data systems provide opportunities for some, but reduce those for others. Mark Graham and Mohammad Amir Anwar examine what happens when online outsourcing platforms create a “planetary labor market.” Although providing opportunities for many people to make money via their Internet connection, Graham and Anwar’s interviews with workers across sub-Saharan Africa demonstrate how “platform work” alters the balance of power between labor and capital. For many low-wage workers across the globe, the platform- and data-driven planetary labor market means downward pressure on wages, fewer opportunities to collectively organize, less worker agency, and less transparency about the nature of the work itself. Moving beyond bold pronouncements that the “world is flat” and big data as empowering, Graham and Anwar show how data-driven systems of employment can act to reduce opportunities for those residing in the poorest parts of the world. The affordances of data and platforms create a planetary labor market for global capital but tie workers ever-more tightly to their own localities. Once again, the valances of global data systems look very different from this “bottom-up” perspective.
Philippa Metcalfe and Lina Dencik shift this conversation from the global movement of labor to that of people, as they write about the implications of European
Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions
HBR Working Paper by Andrea Blasco et al: “Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research….(More)”.
The Economics of Social Data
Paper by Dirk Bergemann and Alessandro Bonatti: “Large internet platforms collect data from individual users in almost every interaction on the internet. Whenever an individual browses a news website, searches for a medical term or for a travel recommendation, or simply checks the weather forecast on an app, that individual generates data. A central feature of the
Trustworthy Privacy Indicators: Grades, Labels, Certifications and Dashboards
Efforts to develop privacy grades, scores, labels, icons, certifications, seals, and dashboards have wrestled with various deficiencies and obstacles for the wide-scale deployment as meaningful and trustworthy privacy indicators. This paper seeks to identify and explain these deficiencies and obstacles that have hampered past and current attempts. With these lessons, the article then offers criteria that will need to be established in law and policy for trustworthy indicators to be successfully deployed and adopted through technological tools. The lack of standardization prevents user-recognizability and dependability in the online marketplace, diminishes the ability to create automated tools for privacy, and reduces incentives for consumers and industry to invest in a privacy indicators. Flawed methods in selection and weighting of privacy evaluation criteria and issues interpreting language that is often ambiguous and vague jeopardize success and reliability when baked into an indicator of privacy protectiveness or invasiveness. Likewise, indicators fall short when those organizations rating or certifying the privacy practices are not objective, trustworthy, and sustainable.
Nonetheless, trustworthy privacy rating systems that are meaningful, accurate, and
Problem Framing Expertise in Public and Social Innovation
Paper by Mieke van