We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more accurately we can recover race or gender from the internal state of the network. Based on this observation, we investigate an alternative fairness approach: we add a second classification head to the network to explicitly predict the protected attribute (such as race or gender) alongside the original task. After training the two-headed network, we enforce demographic parity by merging the two heads, creating a network with the same architecture as the original network. We establish a close relationship between existing approaches and our approach by showing (1) that the decisions of a fair classifier are well-approximated by our approach, and (2) that an unfair and optimally accurate classifier can be recovered from a fair classifier and our second head predicting the protected attribute. We use our explicit formulation to argue that the existing fairness approaches, just as ours, demonstrate disparate treatment and that they are likely to be unlawful in a wide range of scenarios under US law.
Blind Justice: Fairness with Encrypted Sensitive Attributes
University of Cambridge（剑桥大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）；University College London（伦敦大学学院）；Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Disparate Conditional Prediction in Multiclass Classifiers
Department of Computing and Software, McMaster University（计算与软件系，麦斯特大学；加拿大 CIFAR 人工智能主席，向量研究所）；Canada CIFAR AI Chair, Vector Institute（计算机科学系，巴伊兰大学）；Department of Computer Science, Bar-Ilan University（计算机科学系，贝纳-加隆大学）；Department of Computer Science, Ben-Gurion University of the Negev
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing
Department of Statistics and Data Science, Xiamen University（厦门大学统计与数据科学系）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计与数据科学系）；Dept of Statistics and Data Science, University of California, Los Angeles（加州大学洛杉矶分校统计与数据科学系）
