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Data Smells in Public Datasets [article]

Arumoy Shome and Luis Cruz and Arie van Deursen
2022 arXiv   pre-print

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2022 pre-print
arXiv:2203.08007v1 fatcat:ny2g7o56vvalphltrsp7zjk35e
2022 accepted
doi:10.1145/3522664.3528621 arXiv:2203.08007v3 fatcat:vyl34yxipnbfnhwufxedmj53su
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
more » ... 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.
arXiv:2203.08007v2 fatcat:5mhual47krfflg5bjsu3wefxle
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Citation

Arumoy Shome and Luis Cruz and Arie van Deursen. "Data Smells in Public Datasets." arXiv (2022)