Data Smells in Public Datasets [article]

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