--- title: "The Algorithm Doesn’t Know You, but It Decides if You Get Hired." id: "64513" type: "post" slug: "the-algorithm-doesnt-know-you-but-it-decides-if-you-get-hired" published_at: "2026-08-06T04:00:00+00:00" modified_at: "2026-11-01T18:40:02+00:00" url: "https://associationsnow.com/2026/08/06/the-algorithm-doesnt-know-you-but-it-decides-if-you-get-hired/" markdown_url: "https://associationsnow.com/2026/08/06/the-algorithm-doesnt-know-you-but-it-decides-if-you-get-hired.md" excerpt: "Here is something worth sitting with for a moment. Imagine a qualified candidate applying for 10 association jobs. Each application is reviewed by the same AI screening platform. She is rejected from all 10, and no human reviews her file...." taxonomy_category: - "Career Development" taxonomy_post_tag: - "article" - "ASAEcenter" - "Hiring" - "Technology" taxonomy_content_format: - "Article" taxonomy_role: - "Association CEO / Executive Director" - "C-Level" - "Director" - "Employee (Non-Manager)" - "Executive Team" - "For-Profit CEO / President / Owner / Partner" - "Manager" - "VP" taxonomy_persona: - "Executive Leadership" --- [Home](https://associationsnow.com/) » [Articles](https://associationsnow.com/format/article/) » The Algorithm Doesn’t Know You, but It Decides if You Get Hired. Here is something worth sitting with for a moment. Imagine a qualified candidate applying for 10 association jobs. Each application is reviewed by the same AI screening platform. She is rejected from all 10, and no human reviews her file. Recent research shows this is not just theoretical. It is happening now in organizations that believe they are committed to conscious inclusion. The gap between what organizations intend and what actually happens is widening. ### **The Research Every Association Leader Should Know** In May 2026, researchers at Stanford, Chapman, and Northeastern universities published “[Algorithmic Monocultures in Hiring (opens in a new tab)](https://digitaleconomy.stanford.edu/publication/algorithmic-monocultures-in-hiring/.) ,” the largest study so far on AI hiring tools. They reviewed more than 4 million real job applications from 156 employers across 11 industries, all screened by the same AI vendor. The findings were clear: 26 percent of Black applicants and 15 percent of Asian applicants applied for jobs where the AI system produced results that met the federal EEOC definition of adverse impact. If these candidates had been recommended at the same rate as the most-favored group, 40,000 more applications would have advanced. The discrimination did not appear in overall reports. When results were combined across all positions, no differences were observed. An organization could check its quarterly hiring data, see numbers that look fine, report progress to its board, and still use a system that creates EEOC-level bias in the jobs that attract the most underrepresented candidates. Summary data can be good while discrimination is still happening. Both can be true at the same time. ### **One Vendor, Sector-Wide Consequences** The researchers identified an issue that organizations are not addressing: **algorithmic monoculture**. Most employers rely on only a few AI hiring vendors. When organizations use the same platform, a candidate rejected by one faces the same algorithm at the next. When candidates applied to 10 positions screened by the same tool, 4 percent were rejected from every single one. For associations, where the same professionals often apply to several organizations in the same field, this algorithmic monoculture has real consequences. A qualified candidate is not just losing one opportunity; the system is closing doors they do not even know about. ### **This Is a Governance Question, Not a Technology Question** AI screening tools are now used by 90 percent of U.S. employers. Most organizations did not create these tools; they purchased them. That difference is important because during the buying process, several key questions were not asked. Before your organization signs or renews a contract with an AI hiring vendor, make sure you can answer these three questions: - Did you request adverse impact testing for each job position, rather than only looking at overall data before signing? The Stanford study found that combined results can hide problems that appear when you look at each position separately. - Who in your organization is responsible if the system leads to discrimination? If you say the vendor is responsible, that does not provide real accountability and puts your organization at risk. - Did you involve the communities most likely to be affected by this tool when making your choice? The bias mostly showed up in jobs that attracted the largest numbers of Black and Asian candidates, demonstrating why candidate data from those communities should be included and analyzed throughout the testing and review process. These are governance questions. They should be discussed alongside financial oversight, strategic risk, and member accountability. ### **The Window to Act Is Narrowing** The [EU AI Act (opens in a new tab)](https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence) now classifies hiring algorithms as high-risk AI systems, with compliance rules already in place. In the U.S., a federal judge allowed a class action lawsuit in 2025 under the Age Discrimination in Employment Act against an AI hiring platform’s screening tools. The legal environment is changing for every organization using these systems. Most organizations do not intend to create a discriminatory hiring process, yet the Stanford research makes clear that intent and outcome can be very different. The associations that ask the important questions now and act in alignment with their commitments to inclusion will be the ones that can stand behind those commitments when the accountability conversation arrives at their door. [Enid-Mai Jones, CAE](https://associationsnow.com/author/enid-mai-jones-cae/) Enid-Mai Jones, AAiP, CAIC, CDE, CAE, is the founder and CEO of EMJ Consulting LLC and creator of InsightfulAI Strategies. She served on ASAE’s Diversity and Conscious Inclusion Advisory Council and created the GROUND Framework for AI readiness. 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