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Trust Evaluation Problems in Big Data Analytics

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Abstract

This paper considers the problem of trust evaluation in complex computer-aided data analysis. We use a well-known approach that consists of constructing empirical regularities based on measures of use case similarity in the training sample. Trust is approximated by modeling training data with the use of a random sample from an unknown distribution. This approach implements approximate causal analysis and has advantages and disadvantages.

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Authors and Affiliations

  1. Federal Research Center Computer Science and Control, Russian Academy of Sciences, Moscow, Russia

    A. A. Grusho, N. A. Grusho, M. I. Zabezhailo & E. E. Timonina

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  1. A. A. Grusho
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  2. N. A. Grusho
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  3. M. I. Zabezhailo
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  4. E. E. Timonina
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Correspondence to N. A. Grusho.

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Translated by Yu. Kornienko

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Grusho, A.A., Grusho, N.A., Zabezhailo, M.I. et al. Trust Evaluation Problems in Big Data Analytics. Aut. Control Comp. Sci. 56, 847–851 (2022). https://doi.org/10.3103/S0146411622080077

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