Tracing Toxicity Through Code: Towards a Method of Explainability and Interpretability in Software

The ubiquity of digital technologies in citizen’s lives marks a major qualitative shift where automated decisions taken by algorithms deeply affect the lived experience of ordinary people. But this is not just an action-oriented change as computational systems can also introduce epistemological transformations in the constitution of concepts and ideas. However, a lack of public understanding of how algorithms work also makes them a source of distrust, especially concerning the way in which they can be used to create frames or channels for social and individual behaviour. This public concern has been magnified by election hacking, social media disinformation, data extractivism, and a sense that Silicon Valley companies are out of control. The wide adoption of algorithms into so many aspects of peoples’ lives, often without public debate, has meant that increasingly algorithms are seen as mysterious and opaque, when they are not seen as inequitable or biased. Up until recently it has been difficult to challenge algorithms or to question their functioning, especially with wide acceptance that software’s inner workings were incomprehensible, proprietary or secret (cf. open source). Asking why an algorithm did what it did often was not thought particularly interesting outside of a strictly programming context. This meant that there has been a widening explanatory gap in relation to understanding algorithms and their effect on peoples’ lived experiences. This paper argues that Critical Code Studies offers a novel field for developing theoretical and code-epistemological practices to reflect on the explanatory deficit in modern societies from a reliance on information technologies. The challenge of new forms of social obscurity from the implementation of technical systems is heightened by the example of machine learning systems that have emerged in the past decade. A key methodological contribution of this paper is to show how concept formation, in this case of the notion of toxicity, can be traced through key categories and classifications deployed in code structures (e.g. modularity and layering software) but also how these classifications can appear more stable than they actually are by the tendency of software layers to obscure even as they reveal. How a concept such as toxicity can be constituted through code and discourse and then used unproblematically is revealing in relation to both its technical deployment but also for a possible computational sociology of knowledge. By developing a broadened notion of explainability, this paper argues that critical code studies can make important theoretical, code-epistemological and methodological contributions to digital humanities, computer science and related disciplines.