Relying on humans to catch algorithmic errors often fails because of a phenomenon called automation bias. This occurs when people trust computer outputs so much that they stop questioning them. If an AI favors a specific demographic and the recruiter holds the same unconscious preference, they won't see the error as a flaw. Instead, they will see the AI's decision as validation of their own instincts. In this scenario, oversight becomes a feedback loop that strengthens prejudice rather than correcting it.
However, some argue that human intervention remains useful if structured correctly. Proponents of human-in-the-loop systems suggest that trained recruiters can act as a second layer of defense. They argue that humans can recognize context—like a non-traditional career path—that an algorithm might unfairly penalize. This perspective assumes that training can actually reshape human cognition, though skeptics doubt how much training truly changes deep-seated biases.
Ultimately, solving this requires more than just
