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Bertossi, L., & Geerts, F. (2020). Data quality and explainable ai. Journal of Data and Information Quality (JDIQ), 12(2), 1–9. 
Resource type: Journal Article
BibTeX citation key: Bertossi2020
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Categories: Artificial Intelligence, Computer Science, Data Sciences, Engineering, General
Subcategories: Big data, Deep learning, Human factors engineering, Machine intelligence, Machine learning, Psychology of human-AI interaction
Creators: Bertossi, Geerts
Collection: Journal of Data and Information Quality (JDIQ)
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.
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