Abstract
Individuals in civil society are increasingly impacted by algorithmic decision-making but are often unaware that decisions targeting them are delegated to machines. Such unawareness is particularly problematic in the case of faulty decisions and institutional transgressions. How do individuals begin to suspect that they have been targeted by an algorithm and how do they uncover the hidden nature and opacity associated with these systems (their inputs, process, and outputs)? In resource-scarce environments, such as the public sector, noticing such transgressions and tracing them to the algorithm is crucial to protect social justice and public trust in institutions. In this manuscript, we build on the work of von Krogh (2018) on the discovery process of algorithmic decision-making and expand this theory to a non-user perspective exploring algorithmic decision-making from the perspective of the targets of the decisions. Our study contributes to the emergent literature on algorithmic decision-making and the theory on abductive reasoning by pointing at the emotional, cross-layered, and collective work associated with algorithmic discovery.
