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Bias Preservation In Machine Learning: The Legality Of Fairness Metrics Under Eu Non-Discrimination Law, Sandra Wachter, Brent Mittelstadt, Chris Russell

West Virginia Law Review

Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognizing this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias metrics. Often these fairness metrics address technical bias, but not the underlying cause of inequality: social bias. In this Article we make three contributions. First, we assess the compatibility of fairness metrics …