The ability to understand and trust the fairness of model predictions, particularly when considering the outcomes of unprivileged groups, is critical to the deployment and adoption of machine learning systems. SHAP values provide a unified framework for interpreting model predictions and feature attribution but do not address the problem of fairness directly. In this work, we propose a new definition of fairness that emphasises the role of an external auditor and model explicability. To satisfy this definition, we develop a framework for mitigating model bias using regularizations constructed from the SHAP values of an adversarial surrogate model. We focus on the binary classification task with a single unprivileged group and link our fairness explicability constraints to classical statistical fairness metrics. We demonstrate our approaches using gradient and adaptive boosting on: a synthetic dataset, the UCI Adult (Census) dataset and a real-world credit scoring dataset. The models produced were fairer and performant.
Nexa Center for Internet & Society, DAUIN, Politecnico di Torino（都灵理工大学Nexa互联网与社会中心、DAUIN）；Future Urban Legacy Lab, Politecnico di Torino（都灵理工大学未来城市遗产实验室）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most wid…
Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations. We use it to evaluate algorithmic fairness, using the well known COMPAS recidivism data as our example. This approach allows one to identify for each individual in a data set the extent to which they were adversely or beneficially affected by their value of a protected attribute such as their race. The method can do this even if race was not one of the original predictors and even if it does not have access to a proprietary…
Fairness-Aware Federated Learning with Trajectory Shapley Value
Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay（巴黎-萨克雷大学数学系）；Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学数学系）
University of British Columbia（不列颠哥伦比亚大学）；Huawei Technologies Canada Co.（华为技术加拿大公司）；Simon Fraser University（西蒙弗雷泽大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired …
Shapley meets Rawls: an integrated framework for measuring and explaining unfairness
UM6P Airess University Mohammed VI Polytechnic（穆罕默德六世理工大学UM6P Airess大学）；Univ. Nîmes Chrome Um6p, Airess（尼姆大学Chrome实验室，穆罕默德六世理工大学Airess）；Liser Luxembourg（卢森堡社会经济研究所）
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
Department of Information Systems, Arizona State University（信息系统系，亚利桑那州立大学）；Department of Information Systems and Intelligent Business, Xi’an Jiaotong University（信息系统与智能商业系，西安交通大学）
On the use of graph models to achieve individual and group fairness
Department of Statistics. Universidad Carlos III de Madrid（统计系. 皇家卡洛斯三世大学）；uc3m-Santander Big Data Institute. Universidad Carlos III de Madrid（uc3m-桑坦德大数据研究所. 皇家卡洛斯三世大学）
Tel-Aviv University（特拉维夫大学）；Technion—Israel Institute of Technology（以色列理工学院）；Higher School of Economics（高等经济大学）
Machine Learning (ML) algorithms shape our lives. Banks use them to determine if we are good borrowers; IT companies delegate them recruitment decisions; police apply ML for crime-prediction, and judges base their verdicts on ML. However, real-world examples show that such automated decisions tend to discriminate against protected groups. This potential discrimination generated a huge hype both in media and in the research community. Quite a few formal notions of fairness were proposed, which take a form of constraints a "fair" algorithm must satisfy. We focus on scenarios where fairness is im…
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Max Planck Institute for Security and Privacy（马克斯·普朗克安全与隐私研究所）；University of Maryland（马里兰大学）
Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
Department of Computer Science, National University of Singapore, Singapore（新加坡国立大学计算机科学系）；Research (A STAR), Singapore（新加坡A*STAR研究）；Department of Electrical Engineering（电气工程系）；Computer Science, Massachusetts Institute of Technology, USA（美国麻省理工学院计算机科学系）
Brown University（布朗大学）；Harvard University（哈佛大学）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）
As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across various population subgroups including the minority groups. For instance, it should not be the case that explanations associated with instances belonging to a particular gender subgroup (e.g., female) are less accurate than those associated with other genders. However, there is little to no research that assesses if there exist such group-based disparities in the quality of th…
