Limitations Of Fairness In Machine Learning
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Fairness and Machine Learning
An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility. • Introduces the technical and normative foundations of fairness in automated decision-making • Covers the formal and computational methods for characterizing and addressing problems • Provides a critical assessment of their intellectual foundations and practical utility • Features rich pedagogy and extensive instructor resources
Limitations of Fairness in Machine Learning
The issue of socially responsible machine learning has never been more pressing. An entire field of machine learning is dedicated to investigating the societal aspects of automated decision-making systems and providing technical solutions for algorithmic fairness. However, any attempt to improve the fairness of algorithms must be examined under the lens of potential societal harm. In this thesis, we study existing approaches to fair classification and shed light on their various limitations. First, we show that relaxations of fairness constraints used to simplify the learning process of fair models are too coarse, since the final classifier may be distinctly unfair even though the relaxed constraint is satisfied. In response, we propose a new and provably fair method that incorporates the fairness relaxations in a strongly convex formulation. Second, we observe an increased awareness of protected attributes such as race or gender in the last layer of deep neural networks when we regularize them for fair outcomes. Based on this observation, we construct a neural network that explicitly treats input points differently because of protected personal characteristics. With this explicit formulation, we can replicate the predictions of a fair neural network. We argue that both the fair neural network and the explicit formulation demonstrate disparate treatment-a form of discrimination in anti-discrimination laws. Third, we consider fairness properties of the majority vote-a popular ensemble method for aggregating multiple machine learning models to obtain more accurate and robust decisions. We algorithmically investigate worst-case fairness guarantees of the majority vote when it consists of multiple classifiers that are themselves already fair. Under strong independence assumptions on the classifiers, we can guarantee a fair majority vote. Without any assumptions on the classifiers, a fair majority vote cannot be guaranteed in general, but different fairness regimes are possible: on the one hand, using fair classifiers may improve the worst-case fairness guarantees. On the other hand, the majority vote may not be fair at all.
Artificial Intelligence and Machine Learning
This book constitutes the refereed proceedings of the 35th Benelux Conference on Artificial Intelligence and Machine Learning, BNAIC/Benelearn 2023, held in Delft, The Netherlands, during November 8–10, 2023. The 14 papers included in these proceedings were carefully reviewed and selected from 47 submissions. These papers focus on various aspects of Artificial Intelligence and Machine learning, including Natural Language Processing and Reinforcement Learning, and their applications.