Me, Myself and I: Aggregated and Disaggregated Identities on Social Networking Services

In this article I explore some of the legal issues arising from the transformation of SNS operators to providers of digital identity. I consider the implications of the involvement of private sector entities in the field of identity management and discuss some of the privacy implications, as well as the prospects for conciliation between online anonymity and pseudonymity, on the one hand, and the need for identifiability and accountability on the other hand.

Functional Federated Learning in Erlang (ffl-erl)

The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.Comment: 16 pages, accepted for publication in the WFLP 2018 conference proceedings; final post-prin

Big data: Finders keepers, losers weepers?

This article argues that big data’s entrepreneurial potential is based not only on new technological developments that allow for the extraction of non-trivial, new insights out of existing data, but also on an ethical judgment that often remains implicit: namely the ethical judgment that those companies that generate these new insights can legitimately appropriate (the fruits of) these insights. As a result, the business model of big data companies is essentially founded on a libertarian-inspired ‘finders, keepers’ ethic. The article argues, next, that this presupposed ‘finder, keepers’ ethic is far from unproblematic and relies itself on multiple unconvincing assumptions. This leads to the conclusion that the conduct of companies working with big data might lack ethical justification

Institutionalizing Provider-Initiated HIV Testing and Counselling for Children: An Observational Case Study from Zambia

Background: Provider-initiated testing and counselling (PITC) is a priority strategy for increasing access for HIV-exposed children to prevention measures, and infected children to treatment and care interventions. This article examines efforts to scale-up paediatric PITC at a second-level hospital located in Zambia’s Southern Province, and serving a catchment area of 1.2 million people. Methods and Principal Findings: Our retrospective case study examined best practices and enabling factors for rapid institutionalization of PITC in Livingstone General Hospital. Methods included clinical observations, key informant interviews with programme management, and a desk review of hospital management information systems (HMIS) uptake data following the introduction of PITC. After PITC roll-out, the hospital experienced considerably higher testing uptake. In a 36-month period following PITC institutionalization, of total inpatient children eligible for PITC (n = 5074), 98.5 % of children were counselled, and 98.2 % were tested. Of children tested (n = 4983), 15.5 % were determined HIVinfected; 77.6 % of these results were determined by DNA polymerase chain reaction (PCR) testing in children under the age of 18 months. Of children identified as HIV-infected in the hospital’s inpatient and outpatient departments (n = 1342), 99.3 % were enrolled in HIV care, including initiation on co-trimoxazole prophylaxis. A number of good operational practices and enabling factors in the Livingstone General Hospital experience can inform rapid PIT

Market innovation as framing, productive friction and bricolage: an exploration of the personal data market

This paper explores the possibilities offered by recent Science and Technology Studies (STS) research on markets for engaging with market innovation. Although there exist few reflections on how innovation happens in markets, market innovation has not been singularly theorized in STS-inspired market studies. In this paper, we explore the potential analytic utility of different sets of ideas in the field of market studies, such as ‘framing’ [Callon, M. (1998) ‘Introduction: the embeddedness of economic markets in economics’, in The Laws of Markets, ed. M. Callon, Blackwell, Oxford, pp. 1–57; Callon, M. (2007) ‘An essay on the growing contribution of economic markets to the proliferation of the social’, Theory, Culture & Society, vol. 24, no. 7–8, pp. 136–163], ‘productive friction’ [Stark, D. (2009) The Sense of Dissonance: Accounts of Worth in Economic Life, Princeton University Press, Princeton, NJ] and ‘bricolage’ [MacKenzie, D. & Pardo-Guerra, J. P. (2014) ‘Insurgent capitalism: Island, bricolage and the re-making of finance’, Economy and Society, vol. 43, no. 2, pp. 153–182]. Drawing on our research into the online personal data industry and start-ups developing personal data control products, we put together five sensibilities that we think are of use for broader considerations of market innovation

Propaganda in an Age of Algorithmic Personalization: Expanding Literacy Research and Practice

In this commentary, the author considers the rise of algorithmic personalization and the power of propaganda as they shift the dynamic landscape of 21st‐century literacy research and practice. Algorithmic personalization uses data from the behaviors, beliefs, interests, and emotions of the target audience to provide filtered digital content, targeted advertising, and differential product pricing to online users. As persuasive genres, advertising and propaganda may demand different types of reading practices than texts whose purpose is primarily informational or argumentative. Understanding the propaganda function of algorithmic personalization may lead to a deeper consideration of texts that activate emotion and tap into audience values for aesthetic, commercial, and political purposes. Increased attention to algorithmic personalization, propaganda, and persuasion in the context of K–12 literacy education may also help people cope with sponsored content, bots, and other forms of propaganda and persuasion that now circulate online