"Using AI & ML to automate and enhance recruiting" (Anand, 2025)
Used for:
Shaped by cognitive biases, cultural stereotypes & heuristics (Dror, 2020)
Perceived as objective, rule-based & consistent — so people trust it
AI inherits & amplifies historical prejudices rather than reducing them (Martínez et al., 2022)
| Theory | Core Idea | In AI Hiring |
|---|---|---|
| Social Role Theory | Historical divisions → men = leadership, women = support | AI learns certain roles "belong" to a gender |
| System Justification | We want to see systems as fair → automation bias | Recruiters defer to AI over their own gut feeling |
| Stereotype Content Model | Judging on two axes: Competence & Warmth | AI sorts men → leadership, women → support roles |
(Eagly et al., 2026; Nadeem et al., 2021; Jost & Banaji, 1994; Fiske et al., 2002)
Amazon's tool systematically downgraded female résumés using gendered markers as proxies — nobody noticed for years.
Tools are opaque — candidates don't know if AI was used or why they were rejected. Facial data, voice & social media also collected.
A biased system violates human dignity regardless of outcome — the means matter, not just the ends.
Discriminating against half the world can't be outweighed by efficiency gains.
If hiring perpetuates structural inequality, the system fails the fairness test.
Gender bias in AI hiring isn't a tech glitch — it emerges from historical & societal prejudice embedded in training data, org practices, and human decision-making.