Within the planning discourse two poles have materialised over the last decades: a participatory ideal guided by substantive rationality, opposed to an algorithmic governmentality subordinated to instrumental reason. This rift within socialist thought is also observable when it comes to the discovery of needs. The paper understands this discovery procedure primarily as a forecasting problem and demonstrates how many authors dedicated to a participatory planning process call for consumers to write down their desires in the form of wish lists. As (...) a response to this epistemically questionable discovery procedure, the state of the art in capitalist demand-forecasting at enterprises like Amazon is presented, where machine-learning algorithms excel at modelling interrelated time series on a global level by extrapolating demand patterns in real-time. The paper closes with a proposal to reconfigure this predictive apparatus for socialist ends and raises questions concerned with the political implications of centralising decision-making in black-box algorithms. (shrink)

In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: (...) First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward. (shrink)

Presenting the challenges that face digital age citizens as philosophical, as opposed to technological, this paper explores the underpinning structure of digital civics through an overview of its four foundational pillars and their informing scholarship. Through this framework and the literature that supports it, a set of five key concepts are identified as a useful guide for developing digital civics initiatives, including policy and educational interventions. In closing, this work urges the further exploration of the conceptual underpinnings of digital civics (...) to improve future formulations of digital civics and digital citizenship endeavours. (shrink)