Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over time, and exacerbate representation disparity in the long run. In this study, we seek to understand the interplay between ML decisions and the underlying group representation, how they evolve in a sequential framework, and how the use of fairness criteria plays a role in this process. We show that the representation disparity can easily worsen over time under a natural user dynamics (arrival and departure) model when decisions are made based on a commonly used objective and fairness criteria, resulting in some groups diminishing entirely from the sample pool in the long run. It highlights the fact that fairness criteria have to be defined while taking into consideration the impact of decisions on user dynamics. Toward this end, we explain how a proper fairness criterion can be selected based on a general user dynamics model.
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
Gary W. Rollins College of Business, The University of Tennessee at Chattanooga（田纳西大学查塔努加分校加里·W·罗林斯商学院）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Texas A&M University（德克萨斯A&M大学）
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
Carnegie Mellon University（卡内基梅隆大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minority group can shrink over time. In this paper, we first show that the status quo of empirical risk minimization (ERM) amplifies representation disparity over time, which can even make initially fair models unfair. To mitigate this, we develop an approach based on distributionally…
Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
CONICET（阿根廷国家科学技术研究委员会）；Universidad Nacional del Litoral（国立利托拉尔大学）；Cohere For AI（Cohere人工智能研究院）；Institute of Computer Sciences, CONICET & Universidad de Buenos Aires（布宜诺斯艾利斯大学计算机科学研究所（隶属于阿根廷国家科学技术研究委员会））
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
LAAS-CNRS, Université de Toulouse, CNRS（LAAS-CNRS，图卢兹大学，法国国家科学研究中心）；École de Technologie Supérieure（高等工程技术学院）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）
Texas A&M University（德克萨斯农工大学）；Rice University（莱斯大学）；University of Texas Health Science Center（德克萨斯大学健康科学中心）
Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial …
The Price of Ignorance: Information-Free Quotation for Data Retention in Machine Unlearning
RPTU University Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）；Dongbei University of Finance and Economics（东北财经大学）；Tongji University（同济大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Stanford Center for Biomedical Informatics Research（斯坦福生物医学信息学研究中心）；Stanford University（斯坦福大学）
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision-making. We evaluate the interplay between measures of model performance, fairness, and the expected utility of decision-making to offer practical recommendations for the operationalization of algorithmic fairness principles for the development and evaluation of predictive models in healthcare. We conduct an empirical case-study via development of …
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Schwartz Reisman Institute for Technology and Society（施瓦茨·赖斯曼技术与社会研究所）；University of Waterloo（滑铁卢大学）
Higher School of Economics（高等经济学院）；University of Sussex（萨塞克斯大学）；Imperial College London（帝国理工学院）
Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a job interview based on application materials that may include photographs. One promising direction to achieve fairness is by learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair outcomes. All available models however learn latent embeddings which comes at the cost of being uninterpretable. We propose to cast this problem as data-to-data trans…
Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifica…
University of Michigan（密歇根大学）；Lawrence Livermore National Lab（劳伦斯利弗莫尔国家实验室）；RWTH Aachen University（亚琛工业大学）
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i.e., the tendency of linked nodes to have similar attributes. Such assortativity is often induced by homophily, the tendency for nodes of similar properties to connect. Homophily can be common in social networks where systemic factors have forced individuals into communities which share a sensitive attribute. Through synthetic graphs, we study the interplay between locally occurring homoph…
