Improved Adversarial Learning for Fair Classification

Abstract

Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machine learning research. One important paradigm towards this has been providing algorithms for adversarially learning fair classifiers (Zhang et al., 2018; Madras et al., 2018). We formulate the adversarial learning problem as a multi-objective optimization problem and find the fair model using gradient descent-ascent algorithm with a modified gradient update step, inspired by the approach of Zhang et al., 2018. We provide theoretical insight and guarantees that formalize the heuristic arguments presented previously towards taking such an approach. We test our approach empirically on the Adult dataset and synthetic datasets and compare against state of the art algorithms (Celis et al., 2018; Zhang et al., 2018; Zafar et al., 2017). The results show that our models and algorithms have comparable or better accuracy than other algorithms while performing better in terms of fairness, as measured using statistical rate or false discovery rate.

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Cited by in corpus (10)

• Fairness in Machine Learning: A Survey
• Think Locally, Act Globally: Federated Learning with Local and Global Representations
• Fairness for Robust Log Loss Classification
• Fairness with Continuous Optimal Transport
• Algorithm Fairness in AI for Medicine and Healthcare
• Towards a Measure of Individual Fairness for Deep Learning
• Fairness-aware Summarization for Justified Decision-Making
• Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
• Towards Auditability for Fairness in Deep Learning
• A Tale of Fairness Revisited: Beyond Adversarial Learning for Deep Neural Network Fairness