Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917793714143232 |
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| author | de Leoni, Massimiliano Padella, Alessandro |
| author_facet | de Leoni, Massimiliano Padella, Alessandro |
| contents | Predictive business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on biased variables (e.g., gender or nationality), namely variables embodying discrimination. This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased variables. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value. The proposed technique is also compared with the state of the art in fairness in process mining, illustrating that our framework allows for a more enhanced level of fairness, while retaining a better prediction quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02618 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version) de Leoni, Massimiliano Padella, Alessandro Artificial Intelligence Machine Learning J.1 Predictive business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on biased variables (e.g., gender or nationality), namely variables embodying discrimination. This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased variables. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value. The proposed technique is also compared with the state of the art in fairness in process mining, illustrating that our framework allows for a more enhanced level of fairness, while retaining a better prediction quality. |
| title | Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version) |
| topic | Artificial Intelligence Machine Learning J.1 |
| url | https://arxiv.org/abs/2410.02618 |