Quotation Caton, Simon, Malisetty, Saiteja, Haas, Christian. 2022. Impact of Imputation Strategies on Fairness in Machine Learning. Journal of Artificial Intelligence Research. 74 1011-1035.


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Abstract

Research on Fairness and Bias Mitigation in Machine Learning often uses a set of reference datasets for the design and evaluation of novel approaches or definitions. While these datasets are well structured and useful for the comparison of various approaches, they do not reflect that datasets commonly used in real-world applications can have missing values. When such missing values are encountered, the use of imputation strategies is commonplace. However, as imputation strategies potentially alter the distribution of data they can also affect the performance, and potentially the fairness, of the resulting predictions, a topic not yet well understood in the fairness literature. In this article, we investigate the impact of different imputation strategies on classical performance and fairness in classification settings. We find that the selected imputation strategy, along with other factors including the type of classification algorithm, can significantly affect performance and fairness outcomes. The results of our experiments indicate that the choice of imputation strategy is an important factor when considering fairness in Machine Learning. We also provide some insights and guidance for researchers to help navigate imputation approaches for fairness.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Artificial Intelligence Research
Citation Index SCI
WU-Journal-Rating new INF-A
Language English
Title Impact of Imputation Strategies on Fairness in Machine Learning
Volume 74
Year 2022
Page from 1011
Page to 1035
Reviewed? Y
DOI https://doi.org/10.1613/jair.1.13197
Open Access Y
Open Access Link https://www.jair.org/index.php/jair/article/view/13197

Associations

People
Haas, Christian (Details)
External
Caton, Simon (University College Dublin, Ireland)
Malisetty, Saiteja (University of Nebraska Omaha, United States/USA)
Organization
Institute for Corporate Governance IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1122 Artificial intelligence (Details)
1127 Information science (Details)
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