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In our work, we measure the model bias across sub-population distributions in the model output using the Wasserstein metric. To properly quantify the contributions of predictors, we take into account favorability of both the model and predictors with respect to the non-protected class. The quantification is accomplished by the use of transport theory, which gives rise to the decomposition of the model bias and bias explanations to positive and negative contributions. To gain more insight into the role of favorability and allow for additivity of bias explanations, we adapt techniques from cooperative game theory.<\/jats:p>","DOI":"10.1007\/s10994-022-06213-9","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T15:02:58Z","timestamp":1658415778000},"page":"3307-3357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Wasserstein-based fairness interpretability framework for machine learning models"],"prefix":"10.1007","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2669-6336","authenticated-orcid":false,"given":"Alexey","family":"Miroshnikov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2651-0087","authenticated-orcid":false,"given":"Konstandinos","family":"Kotsiopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6007-1017","authenticated-orcid":false,"given":"Ryan","family":"Franks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4498-1800","authenticated-orcid":false,"given":"Arjun","family":"Ravi Kannan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"6213_CR1","doi-asserted-by":"crossref","unstructured":"Aas, K., Jullum, M., & L\u00f8land, A., (2021). 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