In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. This oversimplification of equality through fairness measures is troubling. Many current fairness measures suffer from fairness and performance degradation, or “levelling down,” where fairness is achieved by making every group worse off or by bringing better-performing groups down to the worst off. When fairness can only be achieved by making everyone worse off in material or relational terms through injuries of stigma, loss of solidarity, unequal concern, and missed opportunities for substantive equality, something would appear to have gone wrong in translating the vague concept of ‘fairness’ into practice. In this talk, I will examine the causes and prevalence of levelling down across fairML and explore possible justifications and criticisms based on philosophical and legal theories of equality, distributive justice, and equality law jurisprudence. FairML does not currently engage in the type of measurement, reporting, or analysis necessary to justify levelling down in practice. I will propose a first step towards substantive equality in fairML: “levelling up” systems by design through enforcement of minimum acceptable harm thresholds, or “minimum rate constraints,” as fairness constraints. I will likewise propose an alternative harms-based framework to counter the oversimplified egalitarian framing currently dominant in the field and push future discussion more towards substantive equality opportunities and away from strict egalitarianism by default.
COMPAS Trinity 2023 Seminar Series
Discrimination: A migrant's issue?
Thursdays of Weeks 2 and 4 (4 and 18 May), 3:45 – 4:45 pm (BST)
Conveners: Mariña Fernández-Reino, COMPAS & the Migration Observatory
These seminars will by hybrid. In person at 64 Banbury Road, Oxford, and at this link on Zoom.
Free and all are welcome
