Data Quality and Explainable AI
| Autor: | Floris Geerts, Leopoldo Bertossi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer. Automation
Dirty data Information Systems and Management Computer science Context (language use) 02 engineering and technology Causality Task (project management) 020204 information systems Data quality 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Information Systems Cognitive psychology |
| Zdroj: | ACM journal of data and information quality |
| ISSN: | 1936-1963 1936-1955 |
| DOI: | 10.1145/3386687 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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