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Author (up) Bertossi, L.; Geerts, F. doi  openurl
  Title Data Quality and Explainable AI Type
  Year 2020 Publication Acm Journal Of Data And Information Quality Abbreviated Journal ACM J. Data Inf. Qual.  
  Volume 12 Issue 2 Pages 11  
  Keywords Machine learning; causes; fairness; bias  
  Abstract 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 explainable artificial intelligence. 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.  
  Address [Bertossi, Leopoldo] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: leopoldo.bertossi@uai.cl;  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1936-1955 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000582595600005 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1308  
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