June 2026 BAHREC Newsletter

Coming in like a storm, AI agents are used to perform tasks and improve over time with minimal supervision.  AI assistants are used to answer routine queries via chatbot, and Generative AI creates materials such as training, interview questions, or updating company policies. AI is everywhere and attempts to make work faster, leaner, and more innovative.

AI solutions targeting talent acquisition can help recruiters with sourcing and evaluating candidates during what can be a long, administrative-heavy process.  Algorithms skilled at scanning the internet for candidates that match skill sets, tools that can adjust job descriptions for targeted audiences, and automated systems that move candidates through interviews with ease. While AI tools have made it easier for employers and candidates to move quickly through the application process, AI is changing how companies hire.

A recent study followed 3.4 million people who submitted 4 million job applications to 1,700 job postings across 150 employers and 11 industry sectors to investigate how algorithms impact job seekers. Is the technology we’ve trusted to find our best talent actually screening it out? The findings are stark. Applying the EEOC’s four-fifths rule, the federal standard for detecting hiring discrimination, researchers found clear racial disparities in how the AI screened candidates. One in four Black applicants and one in seven Asian applicants were evaluated by a system, that by legal definition, discriminated against their group. Had the algorithm performed equitably, 40,000 additional applications from Black and Asian candidates would have advanced in the hiring process. 

When most employers rely on the same few AI screening vendors, a candidate rejected by one employer is essentially rejected by all of them. Researchers are calling this an, “algorithmic monoculture,” concentrated reliance on the same systems that produces correlated failures across organizations. Ninety percent of U.S. employers are using a third-party AI screening tools to sort and rank candidates. That level of market concentration mean’s a single vendor’s embedded bias doesn’t disadvantage candidates at one company, it closes doors across an entire industry. Consider not just the applications rejected, but the careers redirected, the innovation not contributed, the perspectives absent from problem-solving tables at scale. 

For talent acquisition leaders who have spent years intentionally building inclusive talent pipelines by expanding sourcing channels, rewriting job descriptions, and training interviewers on structured interview techniques, they need to reckon with the possibility that AI screening tools are functioning as a drain at the front of the pipeline, quietly undoing upstream investment before a single human conversation occurs. For many of us, we are adopting AI hiring tools because we believe they would reduce human bias and that algorithmic consistency would eradicate the subjectivity that has historically disadvantaged candidates from underrepresented groups. However, algorithms will operationalize whatever was embedded in their training data and design choices, at a scale and speed that no human interviewer could match. 

Talent acquisition leaders are standing in front of AI with a reasonable hope that machines might do what humans often struggle to do: set aside bias and evaluate talent based on merit. However, algorithms inherit the world that built them, and in a labor market shaped by decades of unequal access, that inheritance is consequential. The question before inclusive leaders today is, how might we hold technology to a higher standard so that it can help us achieve more equitable outcomes throughout the employee lifecycle?