Data Smells in Public Datasets

Data Smells in Public Datasets

Shome, A. (TU Delft Software Engineering)
Cruz, Luis (TU Delft Software Engineering)
van Deursen, A. (TU Delft Software Technology)

Software Technology

2022

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.

ai engineering
code smells
data quality
data smells

http://resolver.tudelft.nl/uuid:88a2eb26-cc21-4213-88db-406dfcad8f3b

https://doi.org/10.1145/3522664.3528621

IEEE

2022-07-27

978-1-4503-9275-4

Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022

1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022, 2022-05-16 → 2022-05-17, Pittsburgh, United States

Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022

Green 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.

Institutional Repository

conference paper

© 2022 A. Shome, Luis Cruz, A. van Deursen