Thinking with Monsters

Abstract

Optimistic instrumentalism dominates the narratives and discourse(s) that attend recent technology advances. Most future-studies methods in current use depend on projecting properties, processes, facts or conditions of the present into the future. Absent is substantive engagement with the human condition and day-to-day life such technological futures entail. To critique the dominant discourse on future worlds, we offer thinking with monsters to disclose 'livingwith-technologies' and the social, political, and economic alternatives to the optimism that pervades our rational instrumentalism. We argue that shifting the focus away from facts and towards matters of concern engenders a critical voice that enables participation in research that produces the (future) worlds that we seek to explain and understand.

Community Empowerment in Decentralized Autonomous Organizations: A Configurational Approach to Decentralization

“…We acknowledge that futures are not knowable and cannot be limited to extrapolation from known facts, only (Hovorka & Peter, 2018). On the other hand, we can also not delegate all future research to speculative design and design fiction (Tonkinwise, 2014).…”

Scientism as illusio in HR algorithms: Towards a framework for algorithmic hygiene for bias proofing

“…Drawing on Bourdieu's notion of ‘illusio’ (Bourdieu & Wacquant, 1992), the theory of sociomateriality (Orlikowski, 2007), and performativity (Butler, 1990, 1993), we argue that algorithmic decision‐making in HRM, whilst attractive, demands reflexive and flexible reasoning. Accordingly, the aim of the paper is to develop a reflexive and critical inquiry into the assumed objectivity and bias‐free antecedents of algorithmic decision‐making in HR, based on a narrative review of the literature, underscored by a theoretically informed orientation towards algorithmic implementation (Hovorka & Peter, 2018; Lepri et al., 2018). Throughout the paper, algorithmic decision‐making is framed not just as a process that could be enhanced with “better” data, but rather, as a process that involves many actors embedded in subjective perceptions of scientism (Haack, 2011; Kaufman, 2020).…”