{"id":245898,"date":"2026-05-12T08:32:54","date_gmt":"2026-05-12T12:32:54","guid":{"rendered":"https:\/\/today.uconn.edu\/?p=245898"},"modified":"2026-05-20T17:03:51","modified_gmt":"2026-05-20T21:03:51","slug":"uconn-law-professor-examines-how-ai-shapes-the-racial-wealth-gap","status":"publish","type":"post","link":"https:\/\/today.uconn.edu\/2026\/05\/uconn-law-professor-examines-how-ai-shapes-the-racial-wealth-gap\/","title":{"rendered":"UConn Law Professor Examines How AI Shapes the Racial Wealth Gap"},"content":{"rendered":"<figure id=\"attachment_245904\" aria-describedby=\"caption-attachment-245904\" style=\"width: 360px\" class=\"wp-caption alignleft\"><img decoding=\"async\" class=\"size-full wp-image-245904 img-responsive lazyload\" data-src=\"https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/05\/0002_nhumber.jpg\" alt=\"A headshot of a woman\" width=\"360\" height=\"360\" data-srcset=\"https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/05\/0002_nhumber.jpg 360w, https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/05\/0002_nhumber-300x300.jpg 300w, https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/05\/0002_nhumber-150x150.jpg 150w, https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/05\/0002_nhumber-100x100.jpg 100w, https:\/\/today.uconn.edu\/wp-content\/uploads\/2026\/05\/0002_nhumber-275x275.jpg 275w\" data-sizes=\"(max-width: 360px) 100vw, 360px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 360px; --smush-placeholder-aspect-ratio: 360\/360;\" \/><figcaption id=\"caption-attachment-245904\" class=\"wp-caption-text\">Professor Nadiyah J. Humber<\/figcaption><\/figure>\n<p>When UConn Law Professor <a href=\"https:\/\/law.uconn.edu\/person\/nadiyah-humber\/\">Nadiyah J. Humber<\/a> set out to examine artificial intelligence in housing, employment, and lending, she focused on one central question: Does AI affect the racial wealth gap, and if so, how?<\/p>\n<p>In the United States, wealth determines long\u2011term security, influencing housing stability, educational opportunity, and intergenerational mobility. As of 2022, the median White household held approximately $285,000 in wealth, compared to about $45,000 for the median Black household. Because housing, employment, and access to credit are among the primary drivers of wealth\u2011building, the growing use of artificial intelligence in these systems raises urgent questions about whether technology will help close or deepen the divide.<\/p>\n<p>A new report, coauthored by Professor Humber and Professor Yvette Pappoe of the University of the District of Columbia David A. Clarke School of Law and sponsored by <a href=\"https:\/\/civilrights.org\/\">The Leadership Conference on Civil and Human Rights<\/a>, shows how AI tools can unintentionally magnify longstanding inequities when left unchecked. The work reflects UConn Law\u2019s mission of community, equity, and access to justice and underscores the school\u2019s leadership in understanding how emerging technologies shape opportunities in people\u2019s everyday lives.<\/p>\n<p><strong>AI: Enemy or Useful Tool?<\/strong><\/p>\n<p>Across housing, employment, and lending, the research reveals how artificial intelligence increasingly shapes economic opportunity for people of color, often with limited transparency and little room for human judgment.<\/p>\n<p>\u201cArtificial intelligence is not the enemy,\u201d Humber says, reflecting on the report\u2019s findings. \u201cIt\u2019s a tool. But how it\u2019s designed and used matters, and in some cases, it\u2019s reinforcing existing patterns of exclusion.\u201d<\/p>\n<p>Renters described automated tenant\u2011screening systems that were difficult to navigate and offered no opportunity to explain individual circumstances. Job seekers reported submitting numerous applications and hearing back from only a few employers, as automated r\u00e9sum\u00e9 filters relied on criteria shaped by biased data, reflecting how these tools were trained. These decisions often triggered cascading financial strain: housing delays led to credit\u2011card debt, prolonged job searches depleted savings, and each setback harmed credit profiles.<\/p>\n<p><strong>What Comes Next<\/strong><\/p>\n<p>While the findings reveal significant risks, Humber emphasizes that artificial intelligence also presents an opportunity if its use is guided intentionally. The need for these guardrails is underscored by what participants in the study described, such as systems that moved quickly, offered little explanation, and rarely allowed for meaningful review.<\/p>\n<p>\u201cThe opportunity exists,\u201d she says. \u201cIf the tools were designed differently, more inclusively, they could meaningfully address issues around the wealth gap, opportunities for AI literacy, wealth creation, housing, and employment.\u201d The report offers policy recommendations for technology developers, users, and lawmakers, including calls for greater transparency, human review of automated decisions, and the use of more inclusive data models. Sponsored by The Leadership Conference on Civil and Human Rights, the research is informing advocacy efforts and legislative conversations already underway at the national level.<\/p>\n<p><strong>Leading the Conversation on AI and the Law<\/strong><\/p>\n<p>For Humber, ensuring that AI systems promote rather than undermine equitable access is a responsibility that sits squarely within legal education, and one that UConn Law is taking up through research, teaching, and public engagement.<\/p>\n<p>Scholarly work includes <a href=\"https:\/\/law.uconn.edu\/person\/kiel-brennan-marquez\/\">Professor Kiel Brennan\u2011Marquez\u2019<\/a>s examination of the limits of automation in law itself. His research explores whether artificial intelligence can meaningfully replicate forms of legal judgment rooted in discretion, mercy, and moral responsibility, qualities that resist formalization even as legal systems increasingly rely on rule\u2011based technologies.<\/p>\n<p>UConn Law reinforces this intellectual leadership through teaching and practice-based instruction. The curriculum includes courses such as Artificial Intelligence Ethics and Governance, Artificial Intelligence and Social Impact, Cyberlaw, Data Privacy Law, and Cybersecurity and Privacy Compliance, equipping students to understand both the mechanics and consequences of AI\u2011driven decision\u2011making. This past fall, <a href=\"https:\/\/law.uconn.edu\/person\/matthew-lowe\/\">Matthew Lowe<\/a> joined the faculty as a Visiting Professor from Practice, bringing real\u2011world expertise in AI governance, privacy, and cybersecurity shaped by his work as in\u2011house counsel in the technology sector.<\/p>\n<p>Beyond the classroom, the Law School\u2019s <a href=\"https:\/\/ilc.law.uconn.edu\/\">Insurance Law Center<\/a>, led by Director <a href=\"https:\/\/law.uconn.edu\/person\/travis-pantin\/\">Professor Travis Pantin<\/a>, hosted a groundbreaking national conference on AI, Insurance Law, and Regulation, convening leading scholars, regulators, and industry experts to examine how insurance can shape responses to AI risk.<\/p>\n<p>Together, these efforts show how UConn Law is helping to shape real\u2011world conversations about how artificial intelligence should be governed and used in ways that affect people\u2019s everyday lives.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Behind automated housing decisions, r\u00e9sum\u00e9 filters, and credit scores are systems shaping real people\u2019s financial futures. New research highlights how those outcomes can shape whether people are able to build financial stability.<\/p>\n","protected":false},"author":226,"featured_media":157832,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"wds_primary_category":0,"wds_primary_series":0,"wds_primary_attribution":0,"footnotes":""},"categories":[2719,2460,2648,2076,1857,2235],"tags":[],"magazine-issues":[],"coauthors":[2651],"class_list":["post-245898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-faculty","category-blue-research","category-research","category-law","category-today-homepage"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-06-24 04:56:34","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/245898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/users\/226"}],"replies":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/comments?post=245898"}],"version-history":[{"count":4,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/245898\/revisions"}],"predecessor-version":[{"id":245975,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/245898\/revisions\/245975"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media\/157832"}],"wp:attachment":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media?parent=245898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/categories?post=245898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/tags?post=245898"},{"taxonomy":"magazine-issue","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/magazine-issues?post=245898"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/coauthors?post=245898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}