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- W4385981277 abstract "In this work, definitions and approaches in the analysis of fairness and bias in “black box” learning systems were examined from the perspective of unsupervised generative learning. Challenges in direct analysis of fairness and bias in “black box” systems that cannot provide suitable explanations for their decisions have several facets, including: sufficiency and representativity of marked trusted data sets for confident correlation analysis; dimensionality and processing challenges with complex real-world data; and trustworthiness of the trusted data itself. We investigated approaches to fairness audit from the perspective of generative compositional analysis that does not depend on significant amounts of prior data and for that reason can provide additional perspective to the analysis. With generative structure of informative low-dimensional representations that can be obtained with different methods of unsupervised learning, certain forms of bias analysis can be performed without prior data. As demonstrated on a practical example, the significance of the proposed method is that it can provide additional directions and valuable insights in the analysis of bias and fairness of learning systems." @default.
- W4385981277 created "2023-08-19" @default.
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- W4385981277 date "2023-01-01" @default.
- W4385981277 modified "2023-10-14" @default.
- W4385981277 title "Fairness Audit and Compositional Analysis in Trusted AI Program" @default.
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- W4385981277 doi "https://doi.org/10.1007/978-3-031-36118-0_62" @default.
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