AI systems may undermine merit-based hiring and advancement, according to privacy researcher Carissa Veliz

Privacy researcher and author Carissa Veliz, who wrote 'Privacy is Power' and 'Prophecy,' discussed in a new interview how AI systems used in hiring and performance evaluation can reinforce existing biases rather than identify the most qualified candidates — potentially locking people out of opportunities based on patterns in training data rather than actual ability.

Unlike traditional hiring where human judgment can override assumptions, AI-driven screening systems (machine-learning models that rank job candidates automatically) make decisions at scale without transparency, meaning a single algorithmic flaw affects thousands of applicants simultaneously and is harder for individuals to challenge or understand.

For job seekers and employees, this means your resume might be rejected by an AI screener trained on historical data that underrepresented your demographic, or your performance review might be flagged by workplace AI that learned biased patterns — even if you're the most qualified person for the role. For employers, it means trusting AI to do 'meritocratic' hiring may actually backfire by excluding talent and creating legal risk.