Khan, M; (2021) Notes On A (Dis)continuous Surface. [Magazine_article]. Stages , 9

Text Khan - Notes on a (dis)continuous surface.pdf - Published Version Access restricted to UCL open access staff Download (403kB)

Abstract

From content recommendation and social-media feed curation to financial risk assessment and medical diagnoses, machine-learning models have become a pervasive part of our everyday infrastructure. While automated data-processing instruments have long been part of our lives, machine learning provides an accelerated paradigm within which patterns can be unearthed and made actionable across large pools of historical data. Given that these technologies are being deployed in a variety of public and private systems, ethical questions are increasingly being raised when they seem to fail, with particular concern directed at the role that these technologies play in further entrenching racial biases and practices of discrimination. Whether it be failing to recognise darker-skinned subjects,[2] amplifying negative racial stereotypes[3] or denying access to credit, forms of pattern-based learning appear to consistently exacerbate existing racial inequalities and modes of discrimination. With these models increasingly supporting human decision-making in key areas, it is crucial that we understand how racial representation functions within machine-learning systems, asking both how race is understood, and what can be achieved by encoding this understanding.

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