# Data Smells in Public Datasets

arXiv: https://arxiv.org/abs/2203.08007 · HTML: https://mlsanity.com/paper/2203.08007/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [BoostClean: Automated Error Detection and Repair for Machine Learning](https://mlsanity.com/paper/1711.01299/) (2017)
- [Becoming Good at AI for Good](https://mlsanity.com/paper/2104.11757/) (2021)
- [On the experiences of adopting automated data validation in an industrial machine learning project](https://mlsanity.com/paper/2103.04095/) (2021)
- [On the Prevalence, Impact, and Evolution of SQL Code Smells in Data-Intensive Systems](https://mlsanity.com/paper/2201.02215/) (2022)

## Built on by (most cited first, 5 of 5)

- [LLM-based feature generation from text for interpretable machine learning](https://mlsanity.com/paper/2409.07132/) (2024)
- [Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora](https://mlsanity.com/paper/2205.13022/) (2022)
- [What About the Data? A Mapping Study on Data Engineering for AI Systems](https://mlsanity.com/paper/2402.05156/) (2024)
- [Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets](https://mlsanity.com/paper/2606.23204/) (2026)
- [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)

## Abstract (arXiv)

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.