Fairness-Aware Estimation of Graphical Models
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929583073263616 |
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| author | Zhou, Zhuoping Tarzanagh, Davoud Ataee Hou, Bojian Long, Qi Shen, Li |
| author_facet | Zhou, Zhuoping Tarzanagh, Davoud Ataee Hou, Bojian Long, Qi Shen, Li |
| contents | This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the pairwise graph disparity error and a tailored loss function into a nonsmooth multi-objective optimization problem, striving to achieve fairness across different sensitive groups while maintaining the effectiveness of the GMs. Experimental evaluations on synthetic and real-world datasets demonstrate that our framework effectively mitigates bias without undermining GMs' performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17396 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fairness-Aware Estimation of Graphical Models Zhou, Zhuoping Tarzanagh, Davoud Ataee Hou, Bojian Long, Qi Shen, Li Machine Learning This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the pairwise graph disparity error and a tailored loss function into a nonsmooth multi-objective optimization problem, striving to achieve fairness across different sensitive groups while maintaining the effectiveness of the GMs. Experimental evaluations on synthetic and real-world datasets demonstrate that our framework effectively mitigates bias without undermining GMs' performance. |
| title | Fairness-Aware Estimation of Graphical Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.17396 |