Fair learning with private demographic data
http://proceedings.mlr.press/v119/mozannar20a/mozannar20a.pdf WebActive approximately metric-fair learning. in Proc. of the 38th Conference on Uncertainty in Arti cial Intelligence (UAI), 2024. ... Hui Hu*, Zhen Wang* and Chao Lan. A distributed fair machine learning framework with private demographic data protection. International Conference on Data Mining (ICDM), 2024. Zhen Wang* and Chao Lan. Inductive ...
Fair learning with private demographic data
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WebJul 13, 2024 · Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Training an unrestricted neural model might overfit to spurious patterns. To exploit domain-specific knowledge of how past ... 0 11 Metrics Total Citations 0 Total Downloads 11 Last 12 Months 11 Last 6 weeks 3 1 WebDownload scientific diagram A Distributed and Private Fair Learning Framework from publication: A Distributed Fair Machine Learning Framework with Private …
WebWe show how to adapt non-discriminatory learners to work with privatized protected attributes giving theoretical guarantees on performance. Finally, we highlight how the … WebNov 1, 2024 · private fair learning methods on three real-world data sets, and compared them with their existing non-private counterparts. T o facilitate reproduction of the …
WebIn this study, we propose a privacy-preserving training algorithm for a fair support vector machine classifier based on Homomorphic Encryption (HE), where the privacy of both sensitive information and model secrecy can be preserved. WebFair Learning with Private Demographic DataHussein Mozannar, Mesrob Ohannessian, Nathan SrebroSensitive attributes such as race are rarely availabl... Sensitive attributes …
WebFeb 26, 2024 · We show how to adapt non-discriminatory learners to work with privatized protected attributes giving theoretical guarantees on performance. Finally, we highlight …
WebDec 2, 2024 · Deep Learning’s success is made possible in part due to the availability of big datasets – distributed across several owners. To resolve this, researchers propose … ceviche clearwaterWebMay 7, 2024 · Fair learning with private demographic data. In the International Conference on Machine Learning, 2024. Nathan Kallus, Xiaojie Mao, and Angela Zhou. Assessing algorithmic fairness with unobserved protected class using data combination. Management Science, 2024. ceviche cityWebApr 14, 2024 · To address this, we further develop a practical learning algorithm using stochastic gradient methods which incorporates stochastic estimation of the intersectional fairness criteria on minibatches to scale up to big data. Case studies on census data, the COMPAS criminal recidivism dataset, the HHP hospitalization data, and a loan … ceviche close to meWebFeb 26, 2024 · Title: Fair Learning with Private Demographic Data. Authors: Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro. Download PDF Abstract: Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that allows … bve 3200 atcWebSep 17, 2024 · In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data. We assume this data is privately held by a third party, … ceviche clamsWebFeb 7, 2024 · Yan said, “Federated learning has been viewed as a promising solution for collaboratively training machine learning models among multiple parties while maintaining their local data privacy. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as … bve4trainsWebFeb 26, 2024 · Fair Learning with Private Demographic Data 02/26/2024 ∙ by Hussein Mozannar, et al. ∙ 0 ∙ share Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. ceviche co express