ABSTRACT
Research on algorithmic fairness, accountability and transparency promotes a view on algorithmic systems as black boxes that need to be “opened” and “unpacked”. Understanding the black box as a mode of inquiry and knowledge making practice (rather than a thing), this chapter explores what exactly scholars and practitioners aim to unpack when they examine algorithmic black boxes, what they consider to be constitutive elements of these black boxes, and what is “othered” or perceived as “monstrous”. The chapter reviews three distinct modes of assembling black boxes of machine learning (ML)-based systems. Encounters with the outer limits of these ML black boxes explore how social actors, temporalities, places, imaginaries, practices, and values are enfolded in knowledge making about algorithmic regimes.
