Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to review the commonalities, interactions, and contradictions between the dispositions of people and those of machines as they learn from and make sense of each other.

This article explores the question of how to understand social media following the Habermasian theory of the structural transformation of the public sphere. We argue for a return to political-economic fundamentals as the basis for analysing the public sphere and seek to establish a characteristic connection between digital-behavioural control and singularised audiences in the context of proprietary markets. In the digital constellation, it is less a matter of immobilising the citizen as a consumer but rather of their political activation – (...) albeit in conditions under which commercial interests have primacy: privatisation without privatism. (shrink)

Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational (...) reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability. (shrink)

Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped by larger data collectives that precede and continuously change how the individual is represented. We contend that looking beyond the individual is crucial for understanding the factors (...) that can ‘bend’ digital mirrors in specific directions. Since algorithms used for digital profiling are based on historical data, their predictions often inherit and increase the values and perspectives of past data practices. Moreover, the data legacies we leave behind today may return as so-called ‘data phantoms’ that conflict with the interests of the individual and contest who and what the ‘original’ is. (shrink)