Author:Daan Kolkman (Jheronimus Academy of Data Science)
Paper short abstract:
This paper explores the quantification practices through which models and algorithms are created, maintained and contested. It draws on data collected in the analytical industry and government in the UK and the Netherlands to illustrate how non-experts evaluate the credibility of highly technical objects.
Paper long abstract:
The rapid development and dissemination of data science methods, tools and libraries, allows for the development of ever more intricate models and algorithms. Such digital objects are simultaneously the vehicle and outcome of quantification practices and may embody a particular world-view with associated norms and values. More often than not, a set of specific technical skills is required to create, use or interpret these digital objects. As a result, the mechanics of the model or algorithm may be virtually incomprehensible to non-experts.
This is of consequence for the process of knowledge creation because it may introduce power asymmetries and because successful implementation of models and algorithms in an organizational context requires that all those involved have faith in the model or algorithm. This paper contributes to the sociology of quantification by exploring the practices through which non-experts ascertain the quality and credibility of digital objects as myths or fictions. By considering digital objects as myths or fictions, the codified nature of these objects comes into focus.
This permits the illustration of the practices through which experts and non-experts develop, maintain, question or contest such myths. The paper draws on fieldwork conducted in government and analytic industry in the form of interviews, observations and documents to illustrate and contrast the practices which are available to non-experts and experts in bringing about the credibility or incredibility of such myths or fictions. It presents a detailed account of how digital objects become embedded in the organisations that use them.
Critical data studies