Description

Artificial intelligence is now embedded in nearly every stage of the employee lifecycle — resume screening, candidate ranking, video interviews, performance analytics, and promotion modeling. But in the absence of a unified federal AI employment law, U.S. employers are facing a rapidly expanding patchwork of state and local rules that carry real legal, financial, and reputational consequences.

New York City started the wave in 2023 with mandatory bias audits for automated employment decision tools. California followed by amending its FEHA regulations effective October 1, 2025. Illinois and Texas both enacted new AI employment statutes on January 1, 2026. Colorado's algorithmic discrimination law is scheduled for June 30, 2026, with revisions expected. And by January 1, 2027, California employers covered by the CCPA will face entirely new ADMT obligations under the CPPA, including pre-use risk assessments, candidate notices, and annual agency filings.

This webinar cuts through the hype and gives HR, compliance, and legal teams a clear, jurisdiction-by-jurisdiction roadmap for the 2026 AI compliance landscape — covering which laws apply today, what's coming next, how to vet AI vendors, what metrics to track, and how to build a governance program that holds up under regulator scrutiny and litigation.

Areas Covered in This Webinar

• Current U.S. AI employment regulations and why federal preemption remains absent
• New York City's AEDT rule — bias audits and candidate notices
• California's amended FEHA regulations (effective October 1, 2025)
• Illinois and Texas AI employment laws (effective January 1, 2026)
• Colorado's algorithmic discrimination law (effective June 30, 2026) and likely revisions
• California's CPPA ADMT regulations (effective January 1, 2027) — risk assessments, pre-use notices, agency filings
• Disparate treatment vs. disparate impact in an AI context
• Vendor due diligence — bias audits, model inputs, validation, security
• Pre- and post-deployment demographic metric tracking
• Heightened rules on video interviews, facial recognition, and biometric data (Illinois, Maryland)
• The "human in the loop" standard and how to operationalize it
• Documentation and recordkeeping that hold up in audits and litigation
• Multistate compliance strategy under the most stringent applicable jurisdiction

Webinar Agenda

Segment 1 — The Regulatory Landscape
A grounded overview of where U.S. AI employment law stands as of mid-2026. We map the active state and local frameworks, identify what's still in proposal stage, and explain why the absence of federal preemption matters for every multistate employer.

Segment 2 — Jurisdiction Deep Dive
A practical walkthrough of New York City's AEDT rule, California's amended FEHA, the Illinois and Texas 2026 statutes, the upcoming Colorado law, and the California CPPA ADMT regulations effective in 2027. For each, we cover scope, triggers, employer obligations, notice requirements, deadlines, and penalties.

Segment 3 — Algorithmic Discrimination: Old Problem, New Mechanics
Why discrimination law cares less about whether a human or a machine made the decision, and more about the outcome. We unpack the legal theories regulators and plaintiffs' attorneys are using to challenge AI-driven employment decisions, and where employer liability actually attaches.

Segment 4 — Vendor Due Diligence in Practice
The questions you need to put to every AI vendor before signing — about bias audits, model inputs, training data, validation methodology, retraining cadence, security safeguards, and indemnification. Includes a sample due diligence checklist.

Segment 5 — Bias Audits and Metric Tracking
What a defensible bias audit actually looks like. Pre- and post-deployment demographic comparison practices. How to spot adverse impact patterns early and what to do when the data raises a flag.

Segment 6 — High-Risk Tool Categories
Facial recognition, video interview analytics, voice analysis, and biometric tools. State-specific requirements in Illinois and Maryland, and the practical case for treating them with extra caution.

Segment 7 — The Human-in-the-Loop Standard
How to structure human review so it is genuine oversight rather than a rubber stamp. Where final-decision authority should sit. How to document human judgment so it survives discovery.

Segment 8 — Building Your AI Governance Program
The governance architecture employers should be standing up in 2026 — an internal AI committee, vendor inventory, impact assessment process, exception protocol, and supporting policies.

Why Should You Attend

The cost of getting AI compliance wrong has climbed sharply. Class action attorneys now have a working theory of liability against employers who deploy unvetted screening tools, state attorneys general are signaling enforcement priorities, and candidates are increasingly willing to file complaints when they suspect an algorithm drove a rejection. Once a regulator opens an inquiry, the absence of a risk assessment, vendor due diligence file, bias audit, or candidate notice becomes the case against the employer.

At the same time, the upside of getting AI right has never been greater. Used responsibly, AI can standardize evaluation criteria, flag biased language, and shrink time-to-fill in ways traditional processes cannot. Walking away from AI is rarely the answer. Walking into it without governance is the real risk.

This session gives you the framework to do both — comply with every applicable law and continue using AI to drive better hiring and workforce decisions. You will leave with a current legal map, a vendor due diligence checklist, a metric-tracking template, and a governance committee outline.

Who Should Attend

• Chief Human Resources Officers (CHRO)
• Vice Presidents of Human Resources
• Directors of Talent Acquisition
• Heads of People Analytics and HR Technology
• Chief Compliance Officers
• Employment Law Attorneys (in-house counsel)
• EEO Officers
• Chief Privacy Officers
• HR Business Partners (multistate operations)
• AI Governance Leads