Publications

Data Smells in Public Datasets

Authors: Arumoy Shome, Luís Cruz, and Arie van Deursen

Published in: CAIN'22 - 1st International Conference on AI Engineering – Software Engineering for AI.

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.

@INPROCEEDINGS{shome2022data,
author={Arumoy Shome and Lu\'{i}s Cruz and Arie van Deursen},
booktitle={CAIN'22 - 1st International Conference on AI Engineering – Software Engineering for AI}, 
title={Data Smells in Public Datasets}, 
year={2022}}

Read me: Arxiv. DOI: 10.1145/3522664.3528621. 🌍Website.