Data Smells in Public Datasets
This addresses data scientists' need for better tools to detect early signs of problems in machine learning systems, particularly in high-stakes domains, but is incremental as it builds on the concept of code smells.
The study tackled the problem of data quality issues in public datasets by introducing a novel catalogue of data smells, analogous to code smells, and analyzed 25 public datasets to identify 14 specific 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.