When an AI screened your last job application, it likely judged more than your skills—it judged your race, age, disability, and voice without a human ever seeing your resume. This episode exposes how hiring and video-interview algorithms use biased historical data to create a self-reinforcing “digital apartheid” in workplaces—and why that means your next promotion, interview, or application could be decided unfairly by math. Listen to the end to learn the concrete fixes that could stop algorithmic discrimination before it becomes irreversible.

🎯 Key Insights You'll Gain
- ⚖️ Algorithmic amplification of bias: How decades of biased hiring records train AI systems to not only reproduce but magnify discrimination against Black women, older workers, people with accents, and disabled applicants.
- 🔁 The feedback loop problem: Why every automated rejection becomes new training data that makes future AI decisions even more exclusionary—and how that creates entrenched, compounding (intersectional) harms.
- 👩‍💼 Real-world stories that matter: Meet Eleanor (rejected for being “overqualified”), Samira (penalized by a video-interview system for wheelchair-related cues), and Chen (downgraded for a non-native speaking style)—examples that show why this is not hypothetical.
- 🏛️ Why current laws fall short: Why existing civil-rights frameworks and even the EU AI Act struggle to address opaque, high-speed decision systems and the enforcement gaps that leave victims with no practical remedy.
- 🔧 Practical fixes and worker strategies: What organizations, regulators, and communities must do—mandatory explainability, auditable hiring models, shifted legal burdens, funded enforcement teams, and community oversight—and immediate steps job seekers can take now (multiple resume versions, network referrals, demand explanations).

Search tags / topics: AI hiring bias, algorithmic discrimination, intersectional discrimination, resume screening, video-interview AI, explainable AI, AI regulation, EU AI Act, workplace fairness, employment law, accessibility, bias amplification, auditability, career advice.

Length: ~300 words.

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