Algorithms in practice: Comparing web journalism and criminal justice

Abstract

Big Data evangelists often argue that algorithms make decision-making more informed and objective-a promise hotly contested by critics of these technologies. Yet, to date, most of the debate has focused on the instruments themselves, rather than on how they are used. This article addresses this lack by examining the actual practices surrounding algorithmic technologies. Specifically, drawing on multi-sited ethnographic data, I compare how algorithms are used and interpreted in two institutional contexts with markedly different characteristics: web journalism and criminal justice. I find that there are surprising similarities in how web journalists and legal professionals use algorithms in their work. In both cases, I document a gap between the intended and actual effects of algorithms-a process I analyze as ''decoupling.'' Second, I identify a gamut of buffering strategies used by both web journalists and legal professionals to minimize the impact of algorithms in their daily work. Those include foot-dragging, gaming, and open critique. Of course, these similarities do not exhaust the differences between the two cases, which are explored in the discussion section. I conclude with a call for further ethnographic work on algorithms in practice as an important empirical check against the dominant rhetoric of algorithmic power.

A systematic review of algorithm aversion in augmented decision making

“…Brown () and Hafenbrädl, Waeger, Marewski, and Gigerenzer () argue that augmented decision making requires extra motivation because it involves combining multiple judgements rather than the acceptance of a single calculation. This means that the successful implementation of algorithmic decision making requires motivating, or incentivizing, human decision makers to utilize algorithmic aids in order to balance the costs of effort with the benefits of decision performance (Christin, ). Throughout the literature, two types of incentives are prevalent: economic (e.g., monetary incentives for making accurate decisions) and social (e.g., abiding by social norms; maintaining reputation among peers and colleagues).…”

“…More recently, researchers have moved beyond the concept of cognitive style in favor of identifying specific heuristics and biases in human cognition that prevent decision makers from utilizing decision aids effectively. That is, although much attention is given to the opaque, black‐boxed nature of algorithms (Christin, ; Dietvorst et al, ; Eastwood et al, ), research suggests that human decision making operates through a black box of its own: intuition. For instance, decision aiding naturally expects a decision maker to adapt his intuition and/or deliberate analyses, but to do so, he would have to understand descriptively the mental processes underlying his unaided intuitive choice well enough to prescribe how to practically transform that intuition into the ideal judgement (Brown, , p. 217).…”

“…This strategy allowed for the categorization based on focal variables to be corroborated. For example, Christin () was categorized as an article focusing on the environmental variables influencing human‐algorithm interaction, and by noting its use of sociological theory [e.g., Bourdieu's, theory of fields] and publication in Big Data & Society, the categorization was reiterated with increased confidence. Next, proposed causes for algorithm aversion were drawn out from each article by isolating the variable that, when manipulated, resulted in a main effect on algorithm aversion and the utilization of algorithmic advice.…”

“…Brown (2015) and Hafenbrädl, Waeger, Marewski, and Gigerenzer (2016) argue that augmented decision making requires extra motivation because it involves combining multiple judgements rather than the acceptance of a single calculation. This means that the successful implementation of algorithmic decision making requires motivating, or incentivizing, human decision makers to utilize algorithmic aids in order to balance the costs of effort with the benefits of decision performance (Christin, 2017).…”

“…More recently, researchers have moved beyond the concept of cognitive style in favor of identifying specific heuristics and biases in human cognition that prevent decision makers from utilizing decision aids effectively. That is, although much attention is given to the opaque, black-boxed nature of algorithms (Christin, 2017;Dietvorst et al, 2015;Eastwood et al, 2012), research suggests that human decision making operates through a black box of its own: intuition.…”