The Role of AI in Employment Age Discrimination
By Rich Hahn, ACP
In March 2026, my employer’s contract with a government agency expired, and my colleagues and I were placed on furlough and unpaid part-time status. Fortunately, as of the writing of this article, my employer has been awarded a new contract by the same federal agency, and my status will soon change to full-time employee.
While on furlough, since I was not paid, I was able to collect unemployment, which required me to keep a list of positions I applied for every week. During that time, from March to September 2026, I applied for 169 positions. While I did receive several phone screens and at least 10 interviews, there was one incident where I interviewed for a position at a cloud company, where I brought 13 years of relevant experience from Yahoo, but did not receive an offer. That experience prompted me to explore how the use of artificial intelligence (AI) in hiring may contribute to age discrimination for applicants over 40.
According to the YouTube video from Elevate To The Unknown, “AGEISM IN HIRING: Why AI Is Secretly Rejecting You After 40 (It’s Illegal),” AI can create age discrimination in job searching by using biased training data and hidden proxy variables that automatically filter out older candidates.[1] Feeding this data into AI recruitment and applicant tracking systems can cause age discrimination because the system learns from historical hiring patterns. If a company’s past workforce data trends younger, the algorithm may assume younger workers are a better fit and reject or deprioritize older applicants.
AI models that use age proxies can easily infer an applicant’s age by scanning data points like graduation dates, years of experience, or technology proficiencies.[2] AI algorithms are sometimes programmed or trained to reject candidates with more than the required amount of experience, assuming they will be overqualified or demand higher pay (Elevate To The Unknown). According to Heather Tinsley-Fix’s testimony before the US Equal Employment Opportunity Commission (EEOC), automated video interviews, chatbots, or digital assessments may score older applicants lower if they hesitate or interact differently with unfamiliar automated screening interfaces.[3] Research has shown that AI-powered hiring systems can perpetuate age bias, potentially causing qualified older applicants to be overlooked during the screening process.[4]
Opinions are mixed regarding companies using hard cutoff filters, like rejecting anyone who graduated before a certain year. I have seen career experts suggest practices like a “resume Botox,” where you remove graduation dates and trim older work history. Proving algorithmic age discrimination under laws like the Age Discrimination in Employment Act (ADEA) remains difficult due to a lack of transparency from employers (Amber Boyd Law).
Legal protections against AI-driven age discrimination are governed by a combination of existing federal civil rights laws and emerging state and local regulations. While there is no single federal law explicitly dedicated to automated hiring, protections remain grounded in established anti-discrimination laws. These laws apply to automated screening systems just as they do to traditional hiring practices, so employers may still be held liable if their use of AI results in unlawful age discrimination.[5],[6]
The ADEA protects job applicants and employees aged 40 and older. Under the ADEA, certain hiring practices that appear neutral—such as an AI model’s filtering parameters—may constitute unlawful age discrimination if they disproportionately exclude older applicants and are not based on reasonable factors other than age.[7],[8]
Even when employers rely on third-party software for hiring decisions, the EEOC and federal courts recognize that the use of AI does not exempt employers from anti-discrimination laws. Employers may still be held liable for unlawful discrimination resulting from AI-powered hiring systems.[9]
The legal boundaries of AI applicant screening are being shaped by the courts, particularly in cases such as Mobley v. Workday, Inc.[10] In March 2026, the court denied Workday’s motion to dismiss the ADEA claims, allowing those claims to proceed. The court also dismissed certain other claims with permission to amend. The ruling did not determine whether unlawful discrimination had occurred. The court had previously concluded that the ADEA’s disparate-impact protections extend to job applicants and that AI vendors may face liability under an “agent” theory of employment discrimination.[11],[12]
While federal AI policies have shifted—including the EEOC’s withdrawal of certain nonbinding guidance—states and cities have also enacted laws regulating the use of AI in employment decisions.[13] In New York, Local Law 144 prohibits employers from using automated employment decision tools (AEDTs) unless the tools have undergone an independent bias audit within the past year. Employers must also notify candidates when these tools will be used. An amendment to the Illinois Human Rights Act, which took effect on January 1, 2026, explicitly prohibits using AI in ways that discriminate against protected classes. It also requires employers to provide notice regarding AI use in employment decisions and prohibits using ZIP codes as proxies for protected classes.[14],[15]
These are important considerations, particularly regarding how AI can contribute to age discrimination against applicants aged 40 and older. Despite the legal protections outlined above, proving age discrimination remains difficult. Because AI software often operates as a “black box,” with indeterminate decision-making processes, automated rejection emails rarely reveal why an applicant was passed over or which data points influenced the decision.
Richard “Rich” Hahn, ACP, is employed by TechOp Solutions International as a Contract Paralegal and works as a Jr. Freedom of Information Analyst for a federal government agency. Rich is a member of NALA’s Diversity, Equity, & Inclusion (DEI) Committee, a former Affiliated Associations Secretary (2023–24), and a former At Large Director on the NALA Board (2019–23). He currently serves as the NALA Liaison for the Commonwealth of Virginia Paralegal Association (CVPA).
[1] YouTube (2026), Elevate To The Unknown, “AGEISM IN HIRING: Why AI Is Secretly Rejecting You After 40 (It’s Illegal),” https://www.youtube.com/watch?v=DdC0-wdByLQ
[2] Amber Boyd Law (2026), AI in Hiring: Is It Discrimination if an Algorithm Rejects You?, https://amberboydlaw.com/ai-in-hiring-is-it-discrimination-if-an-algorithm-rejects-you/
[3] Heather Tinsley-Fix (2023), Testimony of Heather Tinsley-Fix, US Equal Employment Opportunity Commission, https://www.eeoc.gov/meetings/meeting-january-31-2023-navigating-employment-discrimination-ai-and-automated-systems-new/tinsley-fix
[4] Christopher Harris (2023), Mitigating Age Biases in Resume Screening AI Models, The International FLAIRS Conference Proceedings, 36(1), https://doi.org/10.32473/flairs.36.133236
[5] Jason Tenenbaum (2026), Artificial Intelligence Employment Discrimination: How AI Hiring Tools Create EEOC Compliance Risks in 2026, The Law Office of Jason Tenenbaum, P.C., https://jtnylaw.com/2026/04/artificial-intelligence-employment-discrimination-eeoc-hiring-algorithms/
[6] US Equal Employment Opportunity Commission (2024), What is the EEOC’s role in AI?, https://www.eeoc.gov/sites/default/files/2024-04/20240429_What%20is%20the%20EEOCs%20role%20in%20AI.pdf
[7] Nicholas Creel & Ania Rynarzewska (2026), Why AI Hiring Discrimination Claims Are More Dangerous to Employers Than They Look, Business Law Today, https://www.americanbar.org/groups/business_law/resources/business-law-today/2026-june/ai-hiring-discrimination-claims-employers/
[8] US Equal Employment Opportunity Commission, Age Discrimination, https://www.eeoc.gov/age-discrimination
[9] Owen Davis (2025), AI and Workplace Discrimination: What Employers Need to Know after the EEOC and DOL Rollbacks, Husch Blackwell LLP, https://www.huschblackwell.com/newsandinsights/ai-and-workplace-discrimination-what-employers-need-to-know-after-the-eeoc-and-dol-rollbacks
[10] Louis Lopez (2026), Tracking Legal Trends in Age Discrimination in Employment, Generations Journal, American Society on Aging, https://generations.asaging.org/tracking-legal-trends-in-age-discrimination-in-employment/
[11] Mobley v. Workday, Inc., No. 23-cv-00770-RFL (ND Cal. July 12, 2024), Order Granting in Part and Denying in Part Motion to Dismiss, https://www.clm.com/wp-content/uploads/2024/07/mobley-v.-workday-order.pdf
[12] Mobley v. Workday, Inc., No. 23-cv-00770-RFL (ND Cal. Mar. 6, 2026), Order Granting Motion for Leave to File Amicus Brief and Granting in Part and Denying in Part Motion to Dismiss and to Strike, https://law.justia.com/cases/federal/district-courts/california/candce/3:2023cv00770/408645/267/
[13] Erinn L. Rigney, Ninamarie C. Moore, Isabella F. Sparhawk (2025), The Changing Landscape of AI: Federal Guidance for Employers Reverses Course with New Administration, K&L Gates, https://www.klgates.com/thought-leadership/The-Changing-Landscape-of-AI-Federal-Guidance-for-Employers-Reverses-Course-with-New-Administration-1-31-2025
[14] New York City Department of Consumer and Worker Protection. Automated Employment Decision Tools: Frequently Asked Questions. https://www.nyc.gov/site/dca/businesses/faq-automated-employment-decision-tools.page
[15] Illinois General Assembly. Public Act 103-0804 (House Bill 3773), https://ilga.gov/legislation/PublicActs/View/103-0804
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