# The Impact of Data Preparation on the Fairness of Software Systems

arXiv: https://arxiv.org/abs/1910.02321 · HTML: https://mlsanity.com/paper/1910.02321/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Fair Enough: Searching for Sufficient Measures of Fairness](https://mlsanity.com/paper/2110.13029/) (2021)
- [An Empirical Study of Modular Bias Mitigators and Ensembles](https://mlsanity.com/paper/2202.00751/) (2022)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten](https://mlsanity.com/paper/2405.16798/) (2024)
- [AI Fairness: from Principles to Practice](https://mlsanity.com/paper/2207.09833/) (2022)
- [Navigating Ensemble Configurations for Algorithmic Fairness](https://mlsanity.com/paper/2210.05594/) (2022)
- [A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics](https://mlsanity.com/paper/2306.15567/) (2023)
- [Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education](https://mlsanity.com/paper/2109.07908/) (2021)
- [Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction](https://mlsanity.com/paper/2407.14210/) (2024)
- [Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools](https://mlsanity.com/paper/2409.11489/) (2024)
- [Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?](https://mlsanity.com/paper/2412.15920/) (2024)
- [Language Models That Walk the Talk: A Framework for Formal Fairness Certificates](https://mlsanity.com/paper/2505.12767/) (2025)

## Abstract (arXiv)

Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender. Data preparation is key in any machine learning pipeline, but its effect on fairness is yet to be studied in detail. In this paper, we evaluate how the fairness and effectiveness of the learned models are affected by the removal of the sensitive attribute, the encoding of the categorical attributes, and instance selection methods (including cross-validators and random undersampling). We used the Adult Income and the German Credit Data datasets, which are widely studied and known to have fairness concerns. We applied each data preparation technique individually to analyse the difference in predictive performance and fairness, using statistical parity difference, disparate impact, and the normalised prejudice index. The results show that fairness is affected by transformations made to the training data, particularly in imbalanced datasets. Removing the sensitive attribute is insufficient to eliminate all the unfairness in the predictions, as expected, but it is key to achieve fairer models. Additionally, the standard random undersampling with respect to the true labels is sometimes more prejudicial than performing no random undersampling.