Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management

Guus Dix, Wolfgang Kaltenbrunner, Joeri Tijdink, Govert Valkenburg & Sarah de Rijcke (2020)

Politics and Governance, 8(2), pp. 15-25. Open access

Abstract. Marketization and quantification have become ingrained in academia over the past few decades. The trust in numbers and incentives has led to a proliferation of devices that individualize, induce, benchmark, and rank academic performance. As an instantiation of that trend, this article focuses on the establishment and contestation of ‘algorithmic allocation’ at a Dutch university medical centre. Algorithmic allocation is a form of data-driven automated reasoning that enables university administrators to calculate the overall research budget of a department without engaging in a detailed qualitative assessment of the current content and future potential of its research activities. It consists of a range of quantitative performance indicators covering scientific publications, peer recognition, PhD supervision, and grant acquisition. Drawing on semi-structured interviews, focus groups, and document analysis, we contrast the attempt to build a rationale for algorithmic allocation—citing unfair advantage, competitive achievement, incentives, and exchange—with the attempt to challenge that rationale based on existing epistemic differences between departments. From the specifics of the case, we extrapolate to considerations of epistemic and market fairness that might equally be at stake in other attempts to govern the production of scientific knowledge in a quantitative and market-oriented way.

Extract. “Markets are important economic phenomena but they are equally important for the moral justifications they enable. Sociologists have theorized how markets are linked to particular ‘moral views’ on what binds people together (Boltanski & Thevenot, 2006; Fourcade & Healy 2007). Four main aspects of this ‘market bond’ have been highlighted. Firstly, Boltanski and Thevenot (2006, pp. 196–197) speak of it as a relatively coherent set of normative principles which place much emphasis on ‘competition between beings placed in a state of rivalry’ and on the value of competitive achievement. Second, to get ahead in a competitive environment, people lend themselves ‘willingly to every opportunity to engage in a transaction’ and should thus be enabled to exchange goods and services as they see fit (Boltanski & Thevenot, 2006, p. 200). The third aspect of the market bond is that competition and exchange should induce people to work for the benefits of others—not just for themselves. In that sense, ‘market systems are supposed to provide incentives and opportunities for innovation’ (Fourcade & Healy, 2007, p. 290). Fourth, markets are considered to be a fair allocation system vis-à-vis a system of government interventions that privilege some at the cost of others. Thinking along these lines, ‘competitive economic arrangements are the best defence…against arbitrary interference’ (Fourcade & Healy, 2007, p. 290). In our case, ‘market fairness’ is the analytical term that captures these four normative considerations—competitive achievement, exchange, incentivization, and limiting arbitrary interference—with which researchers and administrators build an organizational rationale to support algorithmic allocation.” (p. 16/17)

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GdL (2020, 15 avril). Algorithmic Allocation: Untangling Rival Considerations of Fairness in Research Management. Économie des conventions. Consulté le 21 mai 2024, à l’adresse https://doi.org/10.58079/n3t4