TITLE: Data Smells in Public Datasets 

ABSTRACT: 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.
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.
Data Smells in Public Datasets
http://arxiv.org/abs/2203.08007v3