This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also discussed.For a reliable and quantitative ...This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also discussed.For a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and classified.However,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness.The privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are unavoidable.The imbalance between algorithms’utility and humanization has further reinforced suchworries.Even for federated learning systems,these constraints on precision AI fairness still exist.Aperspective solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.展开更多
基金the National Academy of Sciences India(NASI),Allahabad,India for the support and to the DirectorNational Institute of Advanced Studies(NIAS),Bengaluru,India for providing the infrastructure facilities to carry out this worksupported by the Shanghai High-Level Base-Building Project for Industrial Technology Innovation.
文摘This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also discussed.For a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and classified.However,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness.The privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are unavoidable.The imbalance between algorithms’utility and humanization has further reinforced suchworries.Even for federated learning systems,these constraints on precision AI fairness still exist.Aperspective solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.
