{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:18:34Z","timestamp":1790882314016,"version":"4.1.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2020,5,3]],"date-time":"2020-05-03T00:00:00Z","timestamp":1588464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2020,6,30]]},"abstract":"<jats:p>\n                    In this work, we provide some insights and develop some ideas, with few technical details, about the role of explanations in Data Quality in the context of data-based machine learning models (ML). In this direction, there are, as expected, roles for causality, and\n                    <jats:italic>explainable artificial intelligence<\/jats:italic>\n                    . The latter area not only sheds light on the models, but also on the data that support model construction. There is also room for defining, identifying, and explaining errors in data, in particular, in ML, and also for suggesting repair actions. More generally, explanations can be used as a basis for defining dirty data in the context of ML, and measuring or quantifying them. We think dirtiness as relative to the ML task at hand, e.g., classification.\n                  <\/jats:p>","DOI":"10.1145\/3386687","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T16:16:16Z","timestamp":1588608976000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["Data Quality and Explainable AI"],"prefix":"10.1145","volume":"12","author":[{"given":"Leopoldo","family":"Bertossi","sequence":"first","affiliation":[{"name":"Univ. Adolfo Ib\u00e1\u00f1ez, Santiago, Chile and RelationalAI Inc., Toronto, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Floris","family":"Geerts","sequence":"additional","affiliation":[{"name":"University of Antwerp, Antwerp, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,5,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2017.01.003"},{"key":"e_1_2_1_2_1","volume-title":"Data Quality: Concepts, Methodologies and Techniques","author":"Batini C.","year":"2016","unstructured":"C. Batini and M. Scannapieco . 2016 . Data Quality: Concepts, Methodologies and Techniques . Second edition, Springer . C. Batini and M. Scannapieco. 2016. Data Quality: Concepts, Methodologies and Techniques. 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