Privacy, AI, and the new rules of recruitment

IAPP Women's Leadership Network Web Conference

Broadcast date: 29 Oct. 2026

Time: 08:00–09:00 PDT, 11:00–12:00 EDT, 16:00–17:00 CET

AI is rapidly changing how employers find, evaluate and select talent. Résumé screening, candidate matching, assessments, interview tools and AI-generated recommendations can make recruiting faster and more consistent—but they can also introduce decisions candidates cannot see, data uses they may not understand and biases employers may struggle to detect.

This session looks at AI-enabled recruitment from both sides of the hiring decision. What should candidates know about how their information is being collected, analyzed and inferred? What should recruiters understand before relying on an AI-generated ranking or recommendation? And what should privacy and AI governance professionals be asking about transparency, bias testing, accessibility, vendor claims and meaningful human oversight?

Through practical scenarios, we will examine where legitimate automation can become automation bias—and why having a recruiter “in the loop” does not necessarily mean a human is meaningfully making the decision. That distinction is already one of the strongest concepts in your existing presentation.

Ultimately, the session asks a deceptively simple question: When AI helps determine who gets an opportunity, how do we know the opportunity is being allocated fairly?

Key takeaways:

• Identify where AI enters the recruitment lifecycle and the personal data, inferred data and automated recommendations that may influence candidate outcomes.
• Evaluate privacy, bias and accessibility risks associated with AI-assisted sourcing, screening, assessment, interviewing and candidate ranking.
• Assess whether human oversight is meaningful, including how automation bias can cause recruiters to defer to AI recommendations rather than independently exercise judgment.
• Identify appropriate approaches to transparency and candidate agency, including notice, accommodation, explanation and opportunities for human reconsideration.
• Develop practical questions for evaluating recruitment technologies and vendors, including data provenance, validation, bias testing, explainability, logging and accountability.