LinkedIn's job recommendation algorithm disadvantaged women, requiring another AI to fix the bias—revealing how supposedly 'neutral' algorithms can systemically sideline entire demographics.
Hiring touches nearly everyone, making the example instantly relatable and provocative. The LinkedIn case showcases the problem, consequence, attempted fix. We must avoid missed opportunities and systemic bias. We must talk about responsibility, transparency, and guardrails. Strong shareability across professional networks elevates engagement potential. From job boards to recruiters, AI decides who gets seen—and who's sidelined. See how a 'neutral' algorithm disadvantaged women and why another AI had to fix it. Bias in, bias out. Accountability matters.
Deep dive into real-world examples and case studies
Evidence-based framework connections and practical applications
Actionable takeaways for immediate implementation
This video directly supports Pillar 1 of the Bridge Framework: Bias & Fairness
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