Why zkML? Because @HumanlyHR just launched an updated conversational AI platform designed to automate chat qualification, phone screenings, and structured video interviews — enabling enterprise-scale hiring. When AI is making first-line hiring decisions, verifying how candidates are assessed becomes critical.

2/ This platform doesn’t just filter resumes. It engages candidates via chat, conducts automated phone screenings, and runs structured video interviews — all built on a unified communication framework. But when evaluation is driven by AI decisions, the core challenge shifts from processing volume to justification of decisions.

3/ The issue: AI-driven hiring often functions as a black-box filter. Recruiters see candidate recommendations, but cannot see: - Which data points influenced the recommendation - Whether assessment criteria were followed - Whether the AI model applied correct logic - Whether any drift or bias occurred For scalable hiring operations, unverified reasoning is a systemic weakness.

4/ That’s where zkML matters: ✅Prove a candidate assessment was generated using the approved model ✅Prove which criteria guided the decision without exposing candidate details ✅Prove institutional policies were enforced in the reasoning chain ✅Enable auditors and HR teams to validate workflows without exposing internal logic zkML transforms AI-driven hiring from automated filtering into verifiable decisioning.

5/ Hiring at scale is no longer about speed — it’s about trusted automation. When AI evaluates thousands of candidates autonomously, proof becomes part of the hiring stack. That’s what @PolyhedraZK is building: AI hiring pipelines where each decision can justify itself.