The Human Signal Gap is the difference between what AI screening tools actually measure and what actually predicts success in your specific roles. AI tools score what they can quantify — speech pace, response structure, facial patterns, keyword density. They cannot reliably measure judgment under pressure, leadership capability, cultural context, or real-world performance. That gap between measurable signals and meaningful human capability is where your best candidates disappear before a human ever sees them and where your organization's legal and talent risk quietly accumulates.

Depending on the platform, AI video interview tools and AI hiring tools typically evaluate two categories of signals. Communication signals: speech pace, vocal consistency, response structure, keyword alignment, and STAR format adherence. Environmental signals: background lighting, camera quality, and facial expression patterns.

These are measurable proxies. They are not direct measures of judgment, leadership capability, or real-world performance. Understanding exactly what your specific tool scores, and what the research says about whether those signals predict job success, is the starting point of every assessment I conduct.

Evaluating AI video screening requires someone who understands both how these models are built and what the verbal and nonverbal human signals they score actually mean. AI Hiring Risk Consultant Dr. Tatiana Teppoeva spent 8 years building predictive AI systems at Microsoft, holds a U.S. AI patent in machine learning and a Harvard MS in Data Science, has conducted over 100 interviews across technical and leadership roles, and is government-certified in nonverbal communication, behavioral analysis, voice analysis, and personality profiling. That combination, technical fluency in AI model architecture plus deep expertise in human signal interpretation, is what makes her AI hiring audits different from a purely technical or purely HR evaluation.

Yes. Several US jurisdictions now impose specific requirements on organizations using AI hiring tools — disclosure obligations, human review requirements, and documentation standards. Candidates have up to 300 days federally, and up to three years in Washington state, to file discrimination claims after an AI-assisted hiring decision. If your process cannot explain rejections in clear job-relevant terms or demonstrate meaningful human oversight, your legal exposure is growing as litigation and regulation accelerate.

AI hiring regulations apply broadly across the US and internationally. What varies is which specific requirements apply to your organization based on where you operate and where your candidates are located.

New York City's Local Law 144 requires independent bias audits for automated employment decision tools. Illinois requires disclosure and consent for AI video interview analysis. Colorado's AI Act, effective January 1, 2027, imposes disclosure, human review access, and three-year recordkeeping requirements. The EU AI Act classifies hiring AI as high-risk and requires documented human oversight. Most organizations are operating across multiple jurisdictions without a clear picture of which requirements apply to them.

This landscape is evolving rapidly. What applies to your organization today may expand significantly within the next 12 months. It is important to stay on top of it to proactively protect your organization from lawsuits, regulatory complaints, and candidate disputes that waste time, drain resources, and damage your reputation as an employer.

Every engagement starts with a free 30-minute strategy call. We discuss your specific situation, which tools you're using, and what's causing your concern. Based on that conversation I'll suggest which assessment makes the most sense or tell you honestly if something else would serve you better.