A Catalog of Fairness-Aware Practices in Machine Learning Engineering
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916700411133952 |
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| author | Voria, Gianmario Sellitto, Giulia Ferrara, Carmine Abate, Francesco De Lucia, Andrea Ferrucci, Filomena Catolino, Gemma Palomba, Fabio |
| author_facet | Voria, Gianmario Sellitto, Giulia Ferrara, Carmine Abate, Francesco De Lucia, Andrea Ferrucci, Filomena Catolino, Gemma Palomba, Fabio |
| contents | Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrimination against minorities. The software engineering community has responded by developing fairness-oriented metrics, empirical studies, and approaches. However, there remains a gap in understanding and categorizing practices for engineering fairness throughout the machine learning lifecycle. This paper presents a novel catalog of practices for addressing fairness in machine learning derived from a systematic mapping study. The study identifies and categorizes 28 practices from existing literature, mapping them onto different stages of the machine learning lifecycle. From this catalog, the authors extract actionable items and implications for both researchers and practitioners in software engineering. This work aims to provide a comprehensive resource for integrating fairness considerations into the development and deployment of machine learning systems, enhancing their reliability, accountability, and credibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16683 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Catalog of Fairness-Aware Practices in Machine Learning Engineering Voria, Gianmario Sellitto, Giulia Ferrara, Carmine Abate, Francesco De Lucia, Andrea Ferrucci, Filomena Catolino, Gemma Palomba, Fabio Software Engineering Machine Learning Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrimination against minorities. The software engineering community has responded by developing fairness-oriented metrics, empirical studies, and approaches. However, there remains a gap in understanding and categorizing practices for engineering fairness throughout the machine learning lifecycle. This paper presents a novel catalog of practices for addressing fairness in machine learning derived from a systematic mapping study. The study identifies and categorizes 28 practices from existing literature, mapping them onto different stages of the machine learning lifecycle. From this catalog, the authors extract actionable items and implications for both researchers and practitioners in software engineering. This work aims to provide a comprehensive resource for integrating fairness considerations into the development and deployment of machine learning systems, enhancing their reliability, accountability, and credibility. |
| title | A Catalog of Fairness-Aware Practices in Machine Learning Engineering |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2408.16683 |