Jul 12, 2020
Individual fairness was proposed to address some of the shortcomings of group fairness. Despite its benefits, it requires a task specific fairness metric that encodes our intuition of what is fair and what is unfair for the ML task at hand. Ambiguity in this metric is the main barrier to wider adoption of individual fairness. In this paper, we present two simple algorithms that learn effective fair metrics from a variety of datasets. We verify empirically that fair training with these metrics leads to improved fairness on three machine learning tasks susceptible to gender and racial biases. We also provide theoretical guarantees on the statistical performance of both approaches.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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