The (in)credibility of algorithmic models to non-experts

Abstract

The rapid development and dissemination of data analysis techniques permits the creation of ever more intricate algorithmic models. Such models are simultaneously the vehicle and outcome of quantification practices and embody a worldview with associated norms and values. A set of specialist skills is required to create, use, or interpret algorithmic models. The mechanics of an algorithmic model may be hard to comprehend for experts and can be virtually incomprehensible to non-experts. This is of consequence because such black boxing can introduce power asymmetries and may obscure bias. This paper explores the practices through which experts and non-experts determine the credibility of algorithmic models. It concludes that (1) transparency to (non-)experts is at best problematic and at worst unattainable; (2) authoritative models may come to dictate what types of policies are considered feasible; (3) several of the advantages attributed to the use of quantifications do not hold in policy making contexts.

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