# Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems

arXiv: https://arxiv.org/abs/2203.10384 · HTML: https://mlsanity.com/paper/2203.10384/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [What can Data-Centric AI Learn from Data and ML Engineering?](https://mlsanity.com/paper/2112.06439/) (2021)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems](https://mlsanity.com/paper/2103.14101/) (2021)
- [Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets](https://mlsanity.com/paper/2108.05935/) (2021)
- [On the experiences of adopting automated data validation in an industrial machine learning project](https://mlsanity.com/paper/2103.04095/) (2021)
- [A Typology of Data Anomalies](https://mlsanity.com/paper/2107.01615/) (2021)
- [Understanding the Challenges and Assisting Developers with Developing Spark Applications](https://mlsanity.com/paper/2103.14177/) (2021)

## Built on by (most cited first, 4 of 4)

- [What About the Data? A Mapping Study on Data Engineering for AI Systems](https://mlsanity.com/paper/2402.05156/) (2024)
- [An investigation of licensing of datasets for machine learning based on the GQM model](https://mlsanity.com/paper/2303.13735/) (2023)
- [From AI Technical Debt to Agentic Technical Debt: A Systematic Mapping of Root Causes and Manifestations in Agentic AI Systems](https://mlsanity.com/paper/2608.01001/) (2026)
- [Specification-delta-driven data governance: an empirical study of the guillemotleftspec-deltaguillemotright as the unit of change in lakehouse data platforms](https://mlsanity.com/paper/2608.19838/) (2026)

## Abstract (arXiv)

High data quality is fundamental for today's AI-based systems. However, although data quality has been an object of research for decades, there is a clear lack of research on potential data quality issues (e.g., ambiguous, extraneous values). These kinds of issues are latent in nature and thus often not obvious. Nevertheless, they can be associated with an increased risk of future problems in AI-based systems (e.g., technical debt, data-induced faults). As a counterpart to code smells in software engineering, we refer to such issues as Data Smells. This article conceptualizes data smells and elaborates on their causes, consequences, detection, and use in the context of AI-based systems. In addition, a catalogue of 36 data smells divided into three categories (i.e., Believability Smells, Understandability Smells, Consistency Smells) is presented. Moreover, the article outlines tool support for detecting data smells and presents the result of an initial smell detection on more than 240 real-world datasets.