乐胖代购免代理版

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As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into\u00a0data-informed ADS<\/jats:italic>,\u00a0which take\u00a0humans in the loop<\/jats:italic>\u00a0in all the data processing steps. Data-informed ADS should deal with data responsibly<\/jats:italic>, guaranteeing nondiscrimination<\/jats:italic>\u00a0with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed.<\/jats:p>","DOI":"10.1007\/s00146-022-01472-5","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T20:48:07Z","timestamp":1654807687000},"page":"721-731","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Fairness & friends in the data science era"],"prefix":"10.1007","volume":"38","author":[{"given":"Barbara","family":"Catania","sequence":"first","affiliation":[]},{"given":"Giovanna","family":"Guerrini","sequence":"additional","affiliation":[]},{"given":"Chiara","family":"Accinelli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"issue":"3","key":"1472_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3310231","volume":"11","author":"S Abiteboul","year":"2019","unstructured":"Abiteboul S, Stoyanovich J (2019) Transparency, fairness, data protection, neutrality: data management challenges in the face of new regulation. 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