{"id":10119,"date":"2024-04-24T13:29:06","date_gmt":"2024-04-24T17:29:06","guid":{"rendered":"https:\/\/nationalfairhousing.org\/?p=10119"},"modified":"2024-04-24T13:29:08","modified_gmt":"2024-04-24T17:29:08","slug":"new-report-outlines-method-for-improving-mortgage-fairness","status":"publish","type":"post","link":"https:\/\/nationalfairhousing.org\/new-report-outlines-method-for-improving-mortgage-fairness\/","title":{"rendered":"New Report Outlines Method for Improving Mortgage Fairness"},"content":{"rendered":"\n<p class=\"content-wrap-item post__paragraph\">FOR IMMEDIATE RELEASE<br>April 24, 2024<br>Contact: Janelle Brevard | jbrevard@nationalfairhousing.org<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\"><strong>Washington, D.C. <\/strong>\u2014 Algorithmic fairness techniques can eliminate bias in mortgage underwriting without compromising accuracy, according to <a href=\"https:\/\/nationalfairhousing.org\/resource\/improving-mortgage-underwriting-and-pricing-outcomes-for-protected-classes-through-distribution-matching\/\">a new report<\/a> by the National Fair Housing Alliance\u2122 (NFHA) and Los Angeles-based fairness-as-a-service fintech company FairPlay AI.<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\">\u201cDisparity in mortgage approval rate persists despite decades of legislation and policy actions aimed at eliminating it. As artificial intelligence becomes more ubiquitous in housing and lending, experts have begun looking for ways to leverage fairness techniques to root out rather than entrench biases in AI-based underwriting systems as a necessity to ensure that AI does not become a technology that perpetuates or exacerbates the legacy of discrimination in housing and lending,\u201d said <strong>Michael Akinwumi, NFHA\u2019s Chief Responsible AI Officer<\/strong>. \u201cWhile accuracy-fairness tradeoff is often wrongly used as a defense against search for less discriminatory models in mortgage underwriting as expected by regulators, our study indicates that it is possible to reduce disparity in mortgage approval rate without undercutting accuracy and profitability. A real-world application of our findings could help eliminate lending bias, expand fair housing opportunities for all, especially for Black and Hispanic applicants, and reduce the racial homeownership gap which is currently wider than it was before the passing of the Fair Housing Act in 1968.\u201d<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\">Through a method known as Distribution Matching (DM), AI-based underwriting models can be constrained so that outcome distribution for any one protected group, like Black or Hispanic applicants, closely resembles the outcome distribution for the corresponding control group, such as White applicants, and improve the fairness of the underwriting model for protected groups without diminishing accuracy for all groups.<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\">&#8220;The lending industry has had a long tradition of thinking that greater fairness and greater profits are mutually exclusive\u2014which is not the case,&#8221; said <strong>Kareem Saleh, Founder &amp; CEO of FairPlay AI<\/strong>. &#8220;Today&#8217;s research gives compelling evidence that, by training algorithms to regard disparities in lending outcomes as another form of error, mortgage approval rates for Black and Hispanic homebuyers can be increased by 5-13% with no corresponding increase in risk.&#8221;<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\">NFHA conducted this study using about six million records representing about four million distinct loans, a dataset courtesy of CoreLogic\u00ae. The dataset contains records of loans originated from December 1987 through January 2021.<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\">As a follow on to this release, NFHA and FairPlay AI will host a webinar tomorrow, April 25, 2024 from 3:00 pm &#8211; 4:30 pm ET to explore the study&#8217;s preliminary findings and present recommendations for future research to expand upon these positive findings.<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\">Click here to <a href=\"https:\/\/nationalfairhousing.org\/wp-content\/uploads\/2024\/04\/Unlocking-Fairness-Final_April-2024.pdf\">read<\/a> the study. Click <a href=\"https:\/\/events.nationalfairhousing.org\/aibreakthrough\">here<\/a> to register for the webinar.<\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\"><\/p>\n\n\n\n<p class=\"content-wrap-item post__paragraph\"><em>The National Fair Housing Alliance (NFHA) is the country\u2019s only national civil rights organization dedicated solely to eliminating all forms of housing and lending discrimination and ensuring equal opportunities for all people. As the trade association for over 170 fair housing and justice-centered organizations and individuals throughout the U.S. and its territories, NFHA works to dismantle longstanding barriers to equity and build diverse, inclusive, well-resourced communities. <\/em><br> <br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>FOR IMMEDIATE RELEASEApril 24, 2024Contact: Janelle Brevard | jbrevard@nationalfairhousing.org Washington, D.C. \u2014 Algorithmic fairness techniques can eliminate bias in mortgage underwriting without compromising accuracy, according to a new report by the National Fair Housing Alliance\u2122 (NFHA) and Los Angeles-based fairness-as-a-service fintech company FairPlay AI. \u201cDisparity in mortgage approval rate persists despite decades of legislation and [&hellip;]<\/p>\n","protected":false},"author":433,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[33,2,31],"tags":[62,45],"class_list":["post-10119","post","type-post","status-publish","format-standard","hentry","category-news-media","category-press-releases","category-ai-tech-equity","tag-artificial-intelligence","tag-fair-lending"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - 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