About
Fair Hire was built on a simple observation: most organizations using AI to screen candidates have no real visibility into how those systems make decisions, and even less documentation of it — until they're facing a claim.
Our approach comes from direct, close-in analysis of how these systems actually operate in contested cases: the gap between algorithmic and human decision-making, the way rejection data gets fragmented across platforms, and the specific documentation gaps that turn into legal exposure. We bring that same level of technical scrutiny to employers and law firms before litigation, not after.
Our background spans seven years of hands-on machine identity and certificate lifecycle security work alongside deep, forensic-level analysis of algorithmic hiring systems — a combination that lets us speak credibly to both the infrastructure and the governance side of AI risk.
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