The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. Data scientists spend the majority of their time studying and wrangling the data, yet tools to aid them with data analysis are lacking. This study identifies the recurrent data quality issues in public datasets. Analogous to code smells, we introduce a novel catalogue of data smells that can be used to indicate early signs of problems or technical debt in machine learning systems. To understand the prevalence of data quality issues in datasets, we analyse 25 public datasets and identify 14 data smells.
|Title of host publication||Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||12|
|Publication status||Published - 2022|
|Event||1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022 - Pittsburgh, United States|
Duration: 16 May 2022 → 17 May 2022
|Name||Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022|
|Conference||1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022|
|Period||16/05/22 → 17/05/22|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- ai engineering
- code smells
- data quality
- data smells