Repairing what's not broken - Algorithm repair manual as reflexivity device

Abstract

In this article, we outline an innovative participatory method for reflexive engagement with algorithmic systems and the underlying processes of datafication that accompany them. Faced with the challenges of thinking critically and reflexively about algorithmic systems, both as non-expert individuals and expert researchers, we develop and elaborate on an approach for engaging participants in thinking with, – through and – about algorithmic artifacts. In developing our approach, we start from the premise that algorithms are always broken, and we devise Repair Manuals as productive reflexivity devices that will enable for reflective and reflexive encounters with algorithmic artifacts. Borrowing from the approaches developed by Shannon Mattern and Joseph Dumit, we take algorithmic data artifacts as entry points to embark on an investigative, self-learning and sense-making journey of the inevitable entanglement between the individuals and the all-encompassing algorithmic systems. The results from our study show that this approach offers valuable opportunities and insights both for educators and for researchers, and can be used equally for empowerment and educational goals.