Regimes of justification in the datafied workplace: The case of hiring

Abstract

The uptake of data-driven hiring systems has introduced important questions about how decisions about who is eligible for jobs, and why, are changing. To explore this, the article draws on interviews with prominent providers of data-driven hiring systems and analyses the way they situate the provision of tools in relation to existing hiring processes, what problems they claim to solve, and the nature of the solutions they provide. While the ideological grounds of datafication have been well-established, privileging data-driven knowledge production as less biased, more objective, and with superior insights than other forms of information-gathering, in hiring, we find legitimisation frames extend to ways in which work and workers should be organised and assessed. Drawing on the notion of ‘regimes of justification’, we argue that such legitimisation frames in turn invoke certain normative expectations about what is just and unjust organised around a vision of the common good.