Computing a Data Dividend

January 1, 2019

Abstract

Quality data is a fundamental contributor to success in statistics and machine learning. If a statistical assessment or machine learning leads to decisions that create value, data contributors may want a share of that value. This paper presents methods to assess the value of individual data samples, and of sets of samples, to apportion value among different data contributors. We use Shapley values for individual samples and Owen values for combined samples, and show that these values can be computed in polynomial time in spite of their definitions having numbers of terms that are exponential in the number of samples.

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Publication Type

Paper

Conference / Journal Name

EC 2019

Authors

Eric Bax

BibTeX


@inproceedings{
    author = {},
    title = {‌Computing a Data Dividend‌},
    booktitle = {Proceedings of EC 2019‌},
    year = {‌2019‌}
}