Analyzing Fairness of Computer Vision and Natural Language Processing Models
Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges,...
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MDPI AG
2025-02-01
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| author | Ahmed Rashed Abdelkrim Kallich Mohamed Eltayeb |
| author_facet | Ahmed Rashed Abdelkrim Kallich Mohamed Eltayeb |
| author_sort | Ahmed Rashed |
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| description | Machine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges, this research utilizes two prominent fairness libraries, Fairlearn by Microsoft and AIF360 by IBM. These libraries offer comprehensive frameworks for fairness analysis, providing tools to evaluate fairness metrics, visualize results, and implement bias mitigation algorithms. The study focuses on assessing and mitigating biases for unstructured datasets using Computer Vision (CV) and Natural Language Processing (NLP) models. The primary objective is to present a comparative analysis of the performance of mitigation algorithms from the two fairness libraries. This analysis involves applying the algorithms individually, one at a time, in one of the stages of the ML lifecycle, pre-processing, in-processing, or post-processing, as well as sequentially across more than one stage. The results reveal that some sequential applications improve the performance of mitigation algorithms by effectively reducing bias while maintaining the model’s performance. Publicly available datasets from Kaggle were chosen for this research, providing a practical context for evaluating fairness in real-world machine learning workflows. |
| format | Article |
| id | doaj-art-e4c43afa3f7446c29caa9a6466725712 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-e4c43afa3f7446c29caa9a64667257122025-08-20T03:43:16ZengMDPI AGInformation2078-24892025-02-0116318210.3390/info16030182Analyzing Fairness of Computer Vision and Natural Language Processing ModelsAhmed Rashed0Abdelkrim Kallich1Mohamed Eltayeb2Department of Physics, Shippensburg University of Pennsylvania, Franklin Science Center, 1871 Old Main Drive, Shippensburg, PA 17257, USADepartment of Physics, Shippensburg University of Pennsylvania, Franklin Science Center, 1871 Old Main Drive, Shippensburg, PA 17257, USADepartment of Data Science, College of Computer and Information Systems, Islamic University of Madinah, Al Jamiah Campus, Madinah 42351, Saudi ArabiaMachine learning (ML) algorithms play a critical role in decision-making across various domains, such as healthcare, finance, education, and law enforcement. However, concerns about fairness and bias in these systems have raised significant ethical and social challenges. To address these challenges, this research utilizes two prominent fairness libraries, Fairlearn by Microsoft and AIF360 by IBM. These libraries offer comprehensive frameworks for fairness analysis, providing tools to evaluate fairness metrics, visualize results, and implement bias mitigation algorithms. The study focuses on assessing and mitigating biases for unstructured datasets using Computer Vision (CV) and Natural Language Processing (NLP) models. The primary objective is to present a comparative analysis of the performance of mitigation algorithms from the two fairness libraries. This analysis involves applying the algorithms individually, one at a time, in one of the stages of the ML lifecycle, pre-processing, in-processing, or post-processing, as well as sequentially across more than one stage. The results reveal that some sequential applications improve the performance of mitigation algorithms by effectively reducing bias while maintaining the model’s performance. Publicly available datasets from Kaggle were chosen for this research, providing a practical context for evaluating fairness in real-world machine learning workflows.https://www.mdpi.com/2078-2489/16/3/182machine learning fairnessbias analysis |
| spellingShingle | Ahmed Rashed Abdelkrim Kallich Mohamed Eltayeb Analyzing Fairness of Computer Vision and Natural Language Processing Models Information machine learning fairness bias analysis |
| title | Analyzing Fairness of Computer Vision and Natural Language Processing Models |
| title_full | Analyzing Fairness of Computer Vision and Natural Language Processing Models |
| title_fullStr | Analyzing Fairness of Computer Vision and Natural Language Processing Models |
| title_full_unstemmed | Analyzing Fairness of Computer Vision and Natural Language Processing Models |
| title_short | Analyzing Fairness of Computer Vision and Natural Language Processing Models |
| title_sort | analyzing fairness of computer vision and natural language processing models |
| topic | machine learning fairness bias analysis |
| url | https://www.mdpi.com/2078-2489/16/3/182 |
| work_keys_str_mv | AT ahmedrashed analyzingfairnessofcomputervisionandnaturallanguageprocessingmodels AT abdelkrimkallich analyzingfairnessofcomputervisionandnaturallanguageprocessingmodels AT mohamedeltayeb analyzingfairnessofcomputervisionandnaturallanguageprocessingmodels |