Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair
Machine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many prior works have attempted to mitigate this “model discrimi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/4/1/11 |
| _version_ | 1797445806775599104 |
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| author | Arashdeep Singh Jashandeep Singh Ariba Khan Amar Gupta |
| author_facet | Arashdeep Singh Jashandeep Singh Ariba Khan Amar Gupta |
| author_sort | Arashdeep Singh |
| collection | DOAJ |
| description | Machine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many prior works have attempted to mitigate this “model discrimination” by updating the training data (pre-processing), altering the model learning process (in-processing), or manipulating the model output (post-processing). However, more work can be done in extending this situation to intersectional fairness, where we consider multiple sensitive parameters (e.g., race) and sensitive options (e.g., black or white), thus allowing for greater real-world usability. Prior work in fairness has also suffered from an accuracy–fairness trade-off that prevents both accuracy and fairness from being high. Moreover, the previous literature has not clearly presented holistic fairness metrics that work with intersectional fairness. In this paper, we address all three of these problems by (a) creating a bias mitigation technique called DualFair and (b) developing a new fairness metric (i.e., AWI, a measure of bias of an algorithm based upon inconsistent counterfactual predictions) that can handle intersectional fairness. Lastly, we test our novel mitigation method using a comprehensive U.S. mortgage lending dataset and show that our classifier, or fair loan predictor, obtains relatively high fairness and accuracy metrics. |
| first_indexed | 2024-03-09T13:32:08Z |
| format | Article |
| id | doaj.art-62779c2b55914f50bcbc4cd14ad98d1d |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-09T13:32:08Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-62779c2b55914f50bcbc4cd14ad98d1d2023-11-30T21:16:56ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902022-03-014124025310.3390/make4010011Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFairArashdeep Singh0Jashandeep Singh1Ariba Khan2Amar Gupta3Floyd B. Buchanan High School, 1560 N Minnewawa Ave, Clovis, CA 93619, USAFloyd B. Buchanan High School, 1560 N Minnewawa Ave, Clovis, CA 93619, USAComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USAComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USAMachine learning (ML) models are increasingly being used for high-stake applications that can greatly impact people’s lives. Sometimes, these models can be biased toward certain social groups on the basis of race, gender, or ethnicity. Many prior works have attempted to mitigate this “model discrimination” by updating the training data (pre-processing), altering the model learning process (in-processing), or manipulating the model output (post-processing). However, more work can be done in extending this situation to intersectional fairness, where we consider multiple sensitive parameters (e.g., race) and sensitive options (e.g., black or white), thus allowing for greater real-world usability. Prior work in fairness has also suffered from an accuracy–fairness trade-off that prevents both accuracy and fairness from being high. Moreover, the previous literature has not clearly presented holistic fairness metrics that work with intersectional fairness. In this paper, we address all three of these problems by (a) creating a bias mitigation technique called DualFair and (b) developing a new fairness metric (i.e., AWI, a measure of bias of an algorithm based upon inconsistent counterfactual predictions) that can handle intersectional fairness. Lastly, we test our novel mitigation method using a comprehensive U.S. mortgage lending dataset and show that our classifier, or fair loan predictor, obtains relatively high fairness and accuracy metrics.https://www.mdpi.com/2504-4990/4/1/11machine learningalgorithmic fairnessbias mitigationmortgage lendingaccuracy–fairness trade-off |
| spellingShingle | Arashdeep Singh Jashandeep Singh Ariba Khan Amar Gupta Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair Machine Learning and Knowledge Extraction machine learning algorithmic fairness bias mitigation mortgage lending accuracy–fairness trade-off |
| title | Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair |
| title_full | Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair |
| title_fullStr | Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair |
| title_full_unstemmed | Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair |
| title_short | Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair |
| title_sort | developing a novel fair loan classifier through a multi sensitive debiasing pipeline dualfair |
| topic | machine learning algorithmic fairness bias mitigation mortgage lending accuracy–fairness trade-off |
| url | https://www.mdpi.com/2504-4990/4/1/11 |
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