Timezone: »
Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamics of population qualification and algorithmic decisions under a partially observed Markov decision problem setting. By characterizing the equilibrium of such dynamics, we analyze the long-term impact of static fairness constraints on the equality and improvement of group well-being. Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions. We also consider possible interventions that can effectively improve group qualification or promote equality of group qualification. Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.
Author Information
Xueru Zhang (University of Michigan)
Ruibo Tu (KTH Royal Institute of Technology)
Yang Liu (UC Santa Cruz)
Mingyan Liu (University of Michigan, Ann Arbor)
Mingyan Liu (M'00, SM'11, F'14) received her Ph.D. Degree in electrical engineering from the University of Maryland, College Park, in 2000. She is currently a professor with the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, and the Peter and Evelyn Fuss Chair of Electrical and Computer Engineering. Her research interests are in optimal resource allocation, performance modeling, sequential decision and learning theory, game theory and incentive mechanisms, with applications to large-scale networked systems, cybersecurity and cyber risk quantification. She has served on the editorial boards of IEEE/ACM Trans. Networking, IEEE Trans. Mobile Computing, and ACM Trans. Sensor Networks. She is a Fellow of the IEEE and a member of the ACM.
Hedvig Kjellstrom (KTH Royal Institute of Technology)
Kun Zhang (CMU)
Cheng Zhang (Microsoft Research, Cambridge, UK)
Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.
More from the Same Authors
-
2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning »
Biwei Huang · Sara Magliacane · Kun Zhang · Danielle Belgrave · Elias Bareinboim · Daniel Malinsky · Thomas Richardson · Christopher Meek · Peter Spirtes · Bernhard Schölkopf -
2020 Poster: On the Role of Sparsity and DAG Constraints for Learning Linear DAGs »
Ignavier Ng · AmirEmad Ghassami · Kun Zhang -
2020 Poster: VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data »
Chao Ma · Sebastian Tschiatschek · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2020 Session: Orals & Spotlights Track 27: Unsupervised/Probabilistic »
Marina Meila · Kun Zhang -
2020 Poster: Learning Strategy-Aware Linear Classifiers »
Yiling Chen · Yang Liu · Chara Podimata -
2020 Poster: A Causal View on Robustness of Neural Networks »
Cheng Zhang · Kun Zhang · Yingzhen Li -
2020 Poster: Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs »
Feng Xie · Ruichu Cai · Biwei Huang · Clark Glymour · Zhifeng Hao · Kun Zhang -
2020 Poster: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations »
Huan Zhang · Hongge Chen · Chaowei Xiao · Bo Li · Mingyan Liu · Duane Boning · Cho-Jui Hsieh -
2020 Spotlight: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations »
Huan Zhang · Hongge Chen · Chaowei Xiao · Bo Li · Mingyan Liu · Duane Boning · Cho-Jui Hsieh -
2020 Spotlight: Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs »
Feng Xie · Ruichu Cai · Biwei Huang · Clark Glymour · Zhifeng Hao · Kun Zhang -
2020 Tutorial: (Track1) Advances in Approximate Inference Q&A »
Yingzhen Li · Cheng Zhang -
2020 Poster: Domain Adaptation as a Problem of Inference on Graphical Models »
Kun Zhang · Mingming Gong · Petar Stojanov · Biwei Huang · QINGSONG LIU · Clark Glymour -
2020 Poster: Optimal Query Complexity of Secure Stochastic Convex Optimization »
Wei Tang · Chien-Ju Ho · Yang Liu -
2020 Tutorial: (Track1) Advances in Approximate Inference »
Yingzhen Li · Cheng Zhang -
2019 Poster: Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness »
Xueru Zhang · Mohammadmahdi Khaliligarekani · Cem Tekin · Mingyan Liu -
2019 Poster: Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck »
Maximilian Igl · Kamil Ciosek · Yingzhen Li · Sebastian Tschiatschek · Cheng Zhang · Sam Devlin · Katja Hofmann -
2019 Poster: Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation »
Ruibo Tu · Kun Zhang · Bo Bertilson · Hedvig Kjellstrom · Cheng Zhang -
2019 Poster: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model »
Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2019 Poster: Triad Constraints for Learning Causal Structure of Latent Variables »
Ruichu Cai · Feng Xie · Clark Glymour · Zhifeng Hao · Kun Zhang -
2019 Poster: Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering »
Biwei Huang · Kun Zhang · Pengtao Xie · Mingming Gong · Eric Xing · Clark Glymour -
2019 Poster: Twin Auxilary Classifiers GAN »
Mingming Gong · Yanwu Xu · Chunyuan Li · Kun Zhang · Kayhan Batmanghelich -
2019 Spotlight: Twin Auxilary Classifiers GAN »
Mingming Gong · Yanwu Xu · Chunyuan Li · Kun Zhang · Kayhan Batmanghelich -
2019 Poster: Likelihood-Free Overcomplete ICA and Applications In Causal Discovery »
Chenwei DING · Mingming Gong · Kun Zhang · Dacheng Tao -
2019 Spotlight: Likelihood-Free Overcomplete ICA and Applications In Causal Discovery »
Chenwei DING · Mingming Gong · Kun Zhang · Dacheng Tao -
2018 Poster: Multi-domain Causal Structure Learning in Linear Systems »
AmirEmad Ghassami · Negar Kiyavash · Biwei Huang · Kun Zhang -
2018 Poster: Causal Discovery from Discrete Data using Hidden Compact Representation »
Ruichu Cai · Jie Qiao · Kun Zhang · Zhenjie Zhang · Zhifeng Hao -
2018 Poster: Modeling Dynamic Missingness of Implicit Feedback for Recommendation »
Menghan Wang · Mingming Gong · Xiaolin Zheng · Kun Zhang -
2017 Poster: Learning Causal Structures Using Regression Invariance »
AmirEmad Ghassami · Saber Salehkaleybar · Negar Kiyavash · Kun Zhang