Most conversations about AI and bias focus on the moment a system goes live. The harder truth is that a system fair at launch can quietly drift as data shifts and workflows evolve. Research from MIT and Stanford has documented meaningful performance degradation in production AI models within months of deployment, and the EEOC has made clear that employers remain liable for discriminatory outcomes even when the decision is automated. The candidate screener that worked in Q1 may be filtering out qualified people by Q4.
Drift is not a technical footnote. It is a leadership issue, a compliance issue, and increasingly, a legal one. A growing patchwork of state and local rules now require ongoing bias audits and other risk management procedures for AI and automated tools. For employers, these laws impose new obligations around transparency, testing, and accountability that vary by jurisdiction and continue to evolve. The bar is no longer whether you tested the system. It is whether your organization has clear ownership, meaningful human oversight, and accountability for the decisions AI influences.
Join OneDigital subject matter experts in HR consulting, employment compliance, and equity for a practical conversation about what to watch for as AI becomes embedded in everyday workforce decisions. This session is a candid discussion from practitioners who help organizations align AI use with sound HR, legal, and people practices.
Finish the webinar with a clearer sense of:
- Where AI systems tend to drift and where risk shows up first
- The right questions to ask about AI outcomes in hiring, pay, and performance
- What meaningful human oversight and accountability looks like in practice
- The rising legal landscape around automated employment decisions
- Why closing the AI literacy gap matters for workforce trust
