{"id":370273,"date":"2021-11-21T00:48:00","date_gmt":"2021-11-20T21:48:00","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/extra-credit-do-ai-powered-lending-algorithms-silently-discriminate-this-initiative-aims-to-find-out\/"},"modified":"2021-11-21T00:48:00","modified_gmt":"2021-11-20T21:48:00","slug":"extra-credit-do-ai-powered-lending-algorithms-silently-discriminate-this-initiative-aims-to-find-out","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/extra-credit-do-ai-powered-lending-algorithms-silently-discriminate-this-initiative-aims-to-find-out\/","title":{"rendered":"#Extra Credit: Do AI-powered lending algorithms silently discriminate? This initiative aims to find out"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a3a9a8796db5\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #dd3333;color:#dd3333\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #dd3333;color:#dd3333\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a3a9a8796db5\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/buradabiliyorum.com\/en\/extra-credit-do-ai-powered-lending-algorithms-silently-discriminate-this-initiative-aims-to-find-out\/#%E2%80%98There_has_been_a_myth_that_algorithms_can_be_completely_neutral_Rohit_Chopra_the_CFPB_director_told_Congress_last_month\" >\u2018There has been a myth that algorithms can be completely neutral,\u2019 Rohit Chopra, the CFPB director told Congress last month<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/buradabiliyorum.com\/en\/extra-credit-do-ai-powered-lending-algorithms-silently-discriminate-this-initiative-aims-to-find-out\/#Jillian_Berman\" >Jillian Berman<\/a><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<p>&#8220;<strong>#Extra Credit: Do AI-powered lending algorithms silently discriminate? This initiative aims to find out<\/strong>&#8221;<\/p>\n<h2 class=\"article__subhead\" itemprop=\"alternativeHeadline\"><span class=\"ez-toc-section\" id=\"%E2%80%98There_has_been_a_myth_that_algorithms_can_be_completely_neutral_Rohit_Chopra_the_CFPB_director_told_Congress_last_month\"><\/span>\n  \u2018There has been a myth that algorithms can be completely neutral,\u2019 Rohit Chopra, the CFPB director told Congress last month<br \/>\n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><\/p>\n<div class=\"column column--full article__content\" role=\"region\" aria-label=\"article body\">\n<div class=\"article__side\">\n<div class=\"container--sticky not-active\">\n<div id=\"cx-next\" data-nosnippet>\n              <\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div id=\"js-article__body\" class=\"article__body article-wrap at16-col16 barrons-article-wrap\" itemprop=\"articleBody\" data-sbid=\"WP-MKTW-0000490702\" role=\"document\">\n<div class=\"barrons-article-ad-wrapper\">\n<div data-track=\"barrons-article-ad-wrap\" class=\"barrons-article-ad sticky_item\">\n<div class=\"barrons-main-article-ad-target sticky_target body_ad\" aria-hidden=\"true\"><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div data-layout=\"\n                inline\" data-layout-mobile=\"\" class=\"\n          media-object\n          type-InsetArticleReader\n              \n              inline\n  article__inset\n          article__inset--type-InsetArticleReader\n              article__inset--inline\n  \"><\/p>\n<div class=\"media-object-article-reader\">\n<div class=\"audioplayer\" data-sbid=\"WP-MKTW-0000490702\" role=\"region\" aria-label=\"Listen to Article\" tabindex=\"-1\" id=\"articlereader\" data-show-title=\"false\" data-theme=\"wsj-article-reader\" data-show-header=\"false\" data-show-subscribe=\"false\" data-ads-enabled=\"true\" data-save-publication=\"false\">\n        <\/div>\n<\/p><\/div>\n<\/p><\/div>\n<p>       <strong><em>Hello and welcome back to MarketWatch\u2019s <\/em><\/strong><strong><em>Extra Credit<\/em><\/strong><strong><em> column, a weekly look at the <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/news\/\" data-internallinksmanager029f6b8e52c=\"2\" title=\"News\" target=\"_blank\" rel=\"noopener\">news<\/a> through the lens of debt.<\/em><\/strong><\/p>\n<p> Our system of providing credit has a well-documented history of discrimination that in many cases has made financing more expensive, predatory or non-existant, for non-white consumers.\u00a0<\/p>\n<div class=\"paywall\">\n       For the past several years, financial <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/technology\/\" data-internallinksmanager029f6b8e52c=\"4\" title=\"Technology\" target=\"_blank\" rel=\"noopener\">technology<\/a>, or fintech, companies, have been touting the potential of artificial intelligence and machine learning to help combat this problem. That promise rests on two main ideas. The first is that leaving a lending decision to an algorithm mitigates the bias that can come with human judgement. The second is that these algorithms have the power to spot good credit risks because they can suck in and process so much more data about an <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/download-scripts-themes-apps\/\" data-internallinksmanager029f6b8e52c=\"9\" title=\"Download Scripts &amp; Themes &amp; Apps\" target=\"_blank\" rel=\"noopener\">app<\/a>licant than traditional formulas, which have discriminatory data baked into their design.\u00a0<\/p>\n<p>Some legal experts and computer scientists have been more wary. Just because something is a machine doesn\u2019t mean it\u2019s free of human biases, they say, as the use of machine learning and artificial intelligence in other areas illustrates. In the criminal justice sphere, for example, use of this type of technology was <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.technologyreview.com\/2019\/01\/21\/137783\/algorithms-criminal-justice-ai\/\" class=\"icon none\">once seen<\/a> as a way to reduce bias in sentencing, but now evidence indicates that the data it pulls in <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing\" class=\"icon none\">reproduces<\/a> already present inequality.\u00a0<\/p>\n<p>One of the takeaways from that example, said David Rubenstein, a professor at Washburn University School of Law, is that \u201cthe use of AI systems won\u2019t necessarily solve the problem and in fact can make it worse.\u201d\u00a0<\/p>\n<p>\u201cYou launder biases from the past into the future, under the auspices of a neutral computer system and then you do it at scale because you can do so many more of these computations,\u201d said Rubenstein, who studies AI regulation.\u00a0\u00a0<\/p>\n<p>This week, we\u2019re digging into the findings of a report that\u2019s being used to work through these questions in the consumer lending context. Though companies and regulators evaluate lending algorithms to test for whether they\u2019re discriminating, the methods they use are rarely public.\u00a0<\/p>\n<p>What makes the <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.relmanlaw.com\/media\/news\/1182_PUBLIC%20Upstart%20Monitorship_2nd%20Report_FINAL.pdf\" class=\"icon none\">report released<\/a> last week different is that it\u2019s working through these thorny issues in documents that everyone can see. It\u2019s the result of an agreement between Upstart<br \/>\n        UPST,<br \/>\n        <bg-quote field=\"percentchange\" format=\"0,000.00%\" channel=\"\/zigman2\/quotes\/223096209\/composite\" class=\"negative\">-8.57%<\/bg-quote><span>,<\/span><br \/>\n       a consumer lending company, the Student Borrower Protection Center, a student loan borrower advocacy group, and the NAACP Legal Defense and Education Fund.\u00a0<\/p>\n<p>Relman Colfax was chosen as an independent monitor for the project, but before we get to what the civil rights firm found, a little background about how we got here.\u00a0<\/p>\n<p><strong>Concerns about educational redlining<\/strong><\/p>\n<div id=\"cx-membership-tile\"><\/div>\n<p>Last year, the Student Borrower Protection Center <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/protectborrowers.org\/wp-content\/uploads\/2020\/02\/Education-Redlining-Report.pdf\" class=\"icon none\">published<\/a> a secret shopping exercise to get a sense of the impact of Upstart\u2019s use of certain educational data in its lending decisions. For the past few years, the organization has been concerned about the implications of using factors, like where someone went to school, their standardized test score and their college major, when pricing a loan.\u00a0<\/p>\n<p>That\u2019s because these attributes are often correlated with race and gender. Inequities in the K-12 school system and stratification in higher education mean that non-white and low-income students are more likely to end up at colleges with fewer resources to get them to and through school and into decent paying jobs.\u00a0<\/p>\n<p>Those outcomes combined with discrimination in the labor market increase the possibility that applicants who attended a historically Black college or university, or a minority serving institution could look like a bigger credit risk in models that use this kind of educational data. Students who attend these schools or who major in a lower paying field like education, are more likely to be non-white or women, respectively, groups the law prohibits financial institutions from discriminating against in lending decisions.\u00a0<\/p>\n<div data-layout=\"inline\n                \" data-layout-mobile=\"\" class=\"\n          media-object\n          type-InsetPullQuote\n            inline\n    scope-web|mobileapps\n  article__inset\n          article__inset--type-InsetPullQuote\n            article__inset--inline\n  \"><\/p>\n<div class=\"wsj-article-pullquote article__inset__pullquote \">\n<p class=\"pullquote-content article__inset__pullquote__quote\">\n        <span class=\"l-qt article__inset__pullquote__mark--left\">\u201c<\/span>\u201cYou launder biases from the past into the future, under the auspices of a neutral computer system and then you do it at scale because you can do so many more of these computations<span class=\"r-qt article__inset__pullquote__mark--right\">\u201d<\/span><\/p>\n<p>        <small><br \/>\n          <span class=\"inset-author article__inset__pullquote__author\">\u2014 David Rubenstein, professor at the Washburn University School of Law<\/span><br \/>\n        <\/small><\/p><\/div>\n<\/p><\/div>\n<p>       To test how these factors played out in Upstart\u2019s model, the Student Borrower Protection Center created hypothetical applicants with the same characteristics, except where they went to school. Each of these applicants applied for a $30,000 student loan refinancing product through Upstart\u2019s platform. The organization found that an applicant from Howard University, an HBCU, and an applicant from New Mexico State University, a Hispanic-serving institution, would pay a higher interest rate than an applicant who attended New York University.\u00a0<\/p>\n<p> At the time, Upstart officials took issue with the report\u2019s methodology, <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.upstart.com\/blog\/upstarts-commitment-to-fair-lending\" class=\"icon none\">describing it<\/a> as\u00a0 \u201cinaccurate and misleading.\u201d They <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.relmanlaw.com\/media\/news\/1089_Upstart%20Initial%20Report%20-%20Final.pdf\" class=\"icon none\">noted that<\/a> the rate quotes were based on submitting the same individual\u2019s credit report over a two-and-a-half month period, during which time their credit score changed. About half of the differences in the quotes could be explained by these changes, they said.\u00a0<\/p>\n<p>The Student Borrower Protection Center countered that changes in the applicant\u2019s credit score didn\u2019t take place during the report period and didn\u2019t change the nature of its findings. (This back-and-forth between the two organizations <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.relmanlaw.com\/media\/news\/1089_Upstart%20Initial%20Report%20-%20Final.pdf\" class=\"icon none\">is detailed<\/a> in Relman Colfax\u2019s first report on the monitoring agreement published in April).\u00a0<\/p>\n<p>The findings caught the attention of the Senate Committee on Banking, Housing and Urban Affairs, which <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.brown.senate.gov\/imo\/media\/doc\/2020-02-13%20Senate%20letter%20to%20Upstart.pdf\" class=\"icon none\">asked Upstart to explain<\/a> how it used educational data to make credit decisions. In its <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.banking.senate.gov\/imo\/media\/doc\/Educational%20data%20-%20appendix.pdf\" class=\"icon none\">response letter<\/a>, Upstart officials said factors like an applicant\u2019s most recent school attended, their highest degree and area of study were among the more than 1,500 variables the company\u2019s model considers. Upstart then placed the school into different groups based on certain data, including average incoming standardized test score, and passed that through the model.\u00a0<\/p>\n<p>That approach anonymized the schools, but it also <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.banking.senate.gov\/imo\/media\/doc\/Review%20-%20Use%20of%20Educational%20Data.pdf\" class=\"icon none\">sparked concern<\/a> from some Senators, because non-white students are overrepresented in schools with lower standardized test scores, in part because of <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.brookings.edu\/blog\/up-front\/2020\/12\/01\/sat-math-scores-mirror-and-maintain-racial-inequity\/\" class=\"icon none\">the correlation<\/a> between standardized test scores, income and race. The concerned lawmakers <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.banking.senate.gov\/newsroom\/minority\/brown-warren-and-harris-call-on-cfpb-to-protect-borrowers-from-discrimination\" class=\"icon none\">wrote to<\/a> the Consumer Financial Protection Bureau to look into whether these practices and practices by other lenders violated the Equal Credit Opportunity Act.\u00a0<\/p>\n<p>Ultimately, Upstart stopped using average incoming standardized test scores to group schools. A few months later, the company, the Student Borrower Protection Center and the Legal Defense Fund agreed to have a third-party monitor test Upstart\u2019s model for fair lending concerns.\u00a0<\/p>\n<p><strong>The monitor\u2019s first detailed findings<\/strong><\/p>\n<p>That testing is ongoing, but last week, the monitor released its first detailed report on its findings so far.\u00a0<\/p>\n<p>Although some APR disparities existed, the monitor didn\u2019t find practically significant differences in pricing between Black, white and Hispanic applicants or men and women. With regards to pricing, \u201cthe monitor confirms or found that whatever issues there may have been in the past, those issues don\u2019t seem to exist,\u201d said Matthew Bruckner, an associate professor at Howard University School of Law. \u201cThat\u2019s really big.\u201d<\/p>\n<p>The report did find that there was a difference in approval ratings for Black and white applicants, \u2014 \u201cless of a win for Upstart,\u201d Bruckner said. These disparities were measured without controlling for legitimate creditworthiness criteria and, on its own, the difference doesn\u2019t constitute a fair lending violation, according to the monitor\u2019s report. But the disparities were both statistically and practically significant, the monitor found. That means that not only were the disparities not explained by chance \u2014 what statistical significance tests for in many contexts \u2014 but they were meaningful.\u00a0<\/p>\n<p>For example, it\u2019s easy to imagine that a court or a regulator may not find a 1% difference in approval ratings between two groups to be meaningful enough to indicate that a model is having a disparate impact on one the groups. But as that difference widens it has more practical impact. Relman Colfax has established its own cutoff based on case law to determine when differences become practically significant.\u00a0\u00a0\u00a0<\/p>\n<p>The difference in approval between white and Black applicants was large enough to meet that threshold and to \u201ctrigger an obligation to investigate,\u201d if there are less discriminatory alternatives to the model Upstart is currently using, the authors wrote in the monitor\u2019s report.\u00a0<\/p>\n<p>Separately, the monitor also looked at whether variables in Upstart\u2019s model are proxies for certain protected groups. Put another way, they were checking to see if the variables\u2019 predictive power come solely or largely from a correlation with race or national origin.<\/p>\n<p>What they found is that none of Upstart\u2019s variables on their own have a high likelihood of functioning as proxies for race or national origin, and that all of them together don\u2019t have a high likelihood of functioning as proxies for race or national origin either. What\u2019s less clear is whether the variables interact with each other in Upstart\u2019s model in a way where they function as proxies for protected groups. \u201cWe cannot eliminate the possibility that proxies exist,\u201d the authors of the report wrote.\u00a0<\/p>\n<p>This finding pushed Relman Colfax to suggest that Upstart weigh the feasibility of using a model that\u2019s easier to understand alongside the benefits of its current model, which could include the model\u2019s performance and the flexibility of the structure to implement improvements on certain fairness metrics.\u00a0<\/p>\n<p>This challenge of balancing a model\u2019s accuracy and interpretability is a key issue companies, regulators and other stakeholders are still sorting through. A more accurate model can result both in better profits for a lender and also could theoretically better identify credit-worthy consumers.<\/p>\n<p>For these models, \u201cwe know what the inputs and the outputs are,\u201d said Rubenstein. \u201cThe problem is that the inner logic of the model that turns inputs into outputs can be a black box because of their sheer complexity.\u201d\u00a0<\/p>\n<p>In the fair lending context, lenders have to give reasons for why a particular loan was denied, but it\u2019s not entirely clear how these complex models will meet that requirement for reason-giving, Rubenstein said.\u00a0<\/p>\n<p>\u201cThat\u2019s very much an open and important question that I think the law will have to resolve at some point,\u201d he said.\u00a0 \u201cThese types of studies undertaken by the monitor might be on a path at beginning to answer those questions.\u201d\u00a0<\/p>\n<p><strong>Agreement provides potential for industry-wide insight<\/strong><\/p>\n<p>Indeed, stakeholders view the approach of the Upstart agreement and its public-facing reports as one that could provide insight on these questions with the potential to be used industry-wide.\u00a0<\/p>\n<p>\u201cThe progress made under this agreement shows that all lenders should be transparent and rigorous about testing their models with independent third parties,\u201d Mike Pierce, the executive director of the Student Borrower Protection Center, <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/protectborrowers.org\/upstart-naacpldf-sbpc-fair-lending-report\/\" class=\"icon none\">said in a statement<\/a>, accompanying the report.\u00a0 \u201cThe process we have chosen to work on with Upstart could help guide the lending industry to set high standards when using new technology and data sources.\u201d\u00a0<\/p>\n<p>Nat Hoopes, vice president and head of public policy and regulatory affairs at Upstart, said in a statement that he hopes the reports serve as \u201ca guide that can help all lenders better understand the obligation to test transparently and to improve on the status quo by relentlessly optimizing models for fairness and inclusion, as well as accuracy.\u201d\u00a0<\/p>\n<p>Gerron Levi, senior vice president and head of government affairs at the American Fintech Council, an industry advocacy group, said the reports and efforts like them could provide the public and regulators with more confidence in lenders\u2019 use of this technology.\u00a0<\/p>\n<p>\u201cThey have ground breaking models,\u201d Levi said of fintech companies using this new technology to make credit decisions. \u201cBut it\u2019s also important that through third-party reviews, through the regulatory framework, that the public have confidence that they are producing fair outcomes.\u201d\u00a0<\/p>\n<p>So far, there is some data indicating that Upstart\u2019s model is doing better at providing financing to creditworthy, but often invisible, borrowers than traditional underwriting criteria. For example, an October analysis of data <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3937438\" class=\"icon none\">provided by Upstart<\/a> found that borrowers with credit scores below 640 who had their loans approved by Upstart had a 60% probability of being rejected by traditional lenders.\u00a0<\/p>\n<p>(One of the authors listed on the paper is an Upstart employee. He set up the data environment for the research and didn\u2019t participate in the analysis, according to Marco Di Maggio, Ogunlesi Family Professor of Finance at Harvard Business School, and another author of the study. Di Maggio added that the company didn\u2019t have any say on the outcome of the research and that he and the third co-author have no financial ties to the company).\u00a0<\/p>\n<p>Upstart\u2019s model was more likely to spot creditworthy borrowers the more traditional formula had missed, even if they had little credit history, in part thanks to data on their jobs \u2014 salaried applicants benefitted more than those doing hourly work \u2014 and their educational attainment, Di Maggio said.\u00a0<\/p>\n<p><strong>Questions remain<\/strong><\/p>\n<p>It\u2019s \u201cterrific\u201d that Upstart\u2019s model is performing better than more traditional underwriting criteria that have been notorious for discriminating against certain protected groups both in credit availability and credit pricing, Bruckner said. \u201cI\u2019m super excited that that\u2019s the case,\u201d he said.<\/p>\n<p>Still, questions remain. For example, it\u2019s unclear from the report how Upstart\u2019s model impacts people at the intersection of certain protected groups, for example, women of color, Rubenstein said. <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/proceedings.mlr.press\/v81\/buolamwini18a\/buolamwini18a.pdf\" class=\"icon none\">Previous research<\/a> on the use of artificial intelligence and machine learning in facial recognition has found that those algorithms perform worse on people of color, especially women of color.\u00a0<\/p>\n<p>\u201cIf you only tested Black versus white and men versus women you wouldn\u2019t have known,\u201d Rubenstein said.\u00a0<\/p>\n<p>Opening that \u201cpandora\u2019s box\u201d of intersectionality does create challenges in terms of deciding which categories are relevant to test and what might be relevant when it comes to the law, Rubenstein said. Still, that doesn\u2019t mean that these questions shouldn\u2019t be investigated, he said.\u00a0\u00a0<\/p>\n<p>\u201cIt\u2019s fair to say that the promise of using artificial intelligence and machine learning systems is to improve equity in lending,\u201d he said. \u201cIt should also be the case that they do have the ability to test for these cross sections.\u201d\u00a0<\/p>\n<p>In addition, just because one company\u2019s model is performing well now doesn\u2019t mean it will perform well in the future, Bruckner said.\u00a0<\/p>\n<p>\u201cThe big issue that I worry about is that models degrade,\u201d he said. That\u2019s particularly concerning, Bruckner said, because in 2017, the Consumer Financial Protection Bureau granted Upstart a No-Action Letter, essentially a document indicating the agency has no present intention to bring an enforcement action against a company over a particular product or service. The agency provided Upstart with a No-Action Letter again in 2020. As part of the No Action Letter program, <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/files.consumerfinance.gov\/f\/documents\/cfpb_upstart-network-inc_no-action-letter_2020-11.pdf\" class=\"icon none\">Upstart agreed<\/a>, among other things, to test its model for adverse impacts by group and provide the agency with the results.\u00a0<\/p>\n<p>\u201cWill the model continue to perform well in the future? Will other companies\u2019 models continue to perform as the Upstart model is performing today,\u201d Bruckner said. \u201cWhy is a private nonprofit consumer watchdog the ones who are doing this? We have a federal consumer protection agency whose job it is to do this and they said we have no present intention to bring an enforcement action.\u201d\u00a0\u00a0<\/p>\n<p>It appears the Biden-era Consumer Financial Protection Bureau will be looking at this issue closely. A CFPB spokesperson wrote in an email that artificial intelligence and machine learning models that use non-traditional data in underwriting \u201care accountable for discriminatory lending outcomes.\u201d The spokesperson added that the agency will \u201cuse all its tools\u201d to prevent these models \u201cfrom entrenching biases in underwriting systems.\u201d\u00a0<\/p>\n<p>During a Congressional <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/financialservices.house.gov\/calendar\/eventsingle.aspx?EventID=408560\" class=\"icon none\">hearing last month<\/a>, Rohit Chopra, the recently-confirmed director of the CFPB, emphasized the need to look carefully at the way lenders\u2019 models use alternative data to make credit decisions. \u201cThere has been a myth that algorithms can be completely neutral,\u201d Chopra said. \u201cIn reality, many of those algorithms reinforce the biases that already exist.\u201d\u00a0<\/p><\/div>\n<\/div><\/div>\n<p><\/p>\n<div class=\"byline article__byline\">\n<p>    <span>By<\/span><\/p>\n<div class=\"author mobile-scrim hasMenu\" data-scrim='{\"type\":\"author\",\"header\":\"Jillian Berman\",\"subhead\":\"The Wall Street Journal\",\"list\":[{\"type\":\"link\",\"icon\":\"bio\",\"url\":\"https:\/\/marketwatch.com\/author\/jillian-berman\",\"text\":\"Biography\"},{\"type\":\"link\",\"icon\":\"twitter\",\"url\":\"https:\/\/twitter.com\/JillianBerman\",\"text\":\"@JillianBerman\"},{\"type\":\"link\",\"icon\":\"email\",\"url\":\"http:\/\/www.marketwatch.com\/news\/mailto:jberman@marketwatch.com\",\"text\":\"jberman@marketwatch.com\"}]}' itemscope itemprop=\"author\" itemtype=\"http:\/\/schema.org\/Person\">\n                <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/marketwatch.com\/author\/jillian-berman\" aria-label=\"Jillian Berman author page\"><\/p>\n<h4 itemprop=\"name\"><span class=\"ez-toc-section\" id=\"Jillian_Berman\"><\/span>Jillian Berman<span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><\/a>\n          <\/div>\n<\/div>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<blockquote><p><strong><span style=\"color: #ff6600;\">If you liked the article, do not forget to share it with your friends. Follow us on\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" href=\"https:\/\/news.google.com\/publications\/CAAqBwgKMLG0nwswvr63Aw\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Google News<\/a><\/span>\u00a0too, click on the star and choose us from your favorites.<\/span><\/strong><\/p><\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\">For forums sites go to <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/forum.buradabiliyorum.com\/\" target=\"_blank\" rel=\"noopener\">Forum.BuradaBiliyorum.Com<\/a><\/span><\/strong><\/p>\n<\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\"><strong>If you want to read more News articles, you can visit our <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/en.buradabiliyorum.com\/news\/\" target=\"_blank\" rel=\"noopener\">News category.<\/a><\/span><\/strong><\/p>\n<\/blockquote>\n<p><span style=\"color: black;\"><a style=\"color: #ff9900;\" href=\"http:\/\/www.marketwatch.com\/news\/story.asp?guid=%7B20C05575-04D4-B545-777C-CE2B326819E9%7D&#038;siteid=rss&#038;rss=1\" target=\"_blank\" rel=\"noopener\">Source<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;#Extra Credit: Do AI-powered lending algorithms silently discriminate? This initiative aims to find out&#8221; \u2018There has been a myth that algorithms can be completely neutral,\u2019 Rohit Chopra, the CFPB director told Congress last month Hello and welcome back to MarketWatch\u2019s Extra Credit column, a weekly look at the news through the lens of debt. 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