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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16088 |
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| _version_ | 1866929478227197952 |
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| author | Shinde, Saish |
| author_facet | Shinde, Saish |
| contents | Concerns regarding fairness and bias have been raised in recent years due to the growing use of machine learning models in crucial decision-making processes, especially when it comes to delicate characteristics like gender. In order to address biases in machine learning models, this research paper investigates advanced bias mitigation techniques, with a particular focus on counterfactual fairness in conjunction with data augmentation. The study looks into how these integrated approaches can lessen gender bias in the financial industry, specifically in loan approval procedures. We show that these approaches are effective in achieving more equitable results through thorough testing and assessment on a skewed financial dataset. The findings emphasize how crucial it is to use fairness-aware techniques when creating machine learning models in order to guarantee morally righteous and impartial decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensuring Equitable Financial Decisions: Leveraging Counterfactual Fairness and Deep Learning for Bias Shinde, Saish Machine Learning Artificial Intelligence Concerns regarding fairness and bias have been raised in recent years due to the growing use of machine learning models in crucial decision-making processes, especially when it comes to delicate characteristics like gender. In order to address biases in machine learning models, this research paper investigates advanced bias mitigation techniques, with a particular focus on counterfactual fairness in conjunction with data augmentation. The study looks into how these integrated approaches can lessen gender bias in the financial industry, specifically in loan approval procedures. We show that these approaches are effective in achieving more equitable results through thorough testing and assessment on a skewed financial dataset. The findings emphasize how crucial it is to use fairness-aware techniques when creating machine learning models in order to guarantee morally righteous and impartial decision-making. |
| title | Ensuring Equitable Financial Decisions: Leveraging Counterfactual Fairness and Deep Learning for Bias |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16088 |