AI in hiring: Systemic barriers persist despite algorithmic fixes, reinforcing corporate power over marginalised workers
Original framing: “From bias to balance: How AI can reshape hiring decisions” — Phys.org
Studies show AI hiring tools can reduce overt discrimination but often encode historical biases in training data, disproportionately excluding disabled and racialised applicants. The 'fairness' metrics used (e.g., demographic parity) are contested, as they may obscure deeper structural inequities. Peer-reviewed research highlights the lack of transparency in algorithmic hiring, making it difficult to audit systemic biases.
The AI hiring narrative exemplifies how techno-solutionism obscures structural power imbalances, framing discrimination as a technical flaw rather than a product of colonial, ableist, and capitalist hiring practices.
While inclusion-focused AI may reduce overt bias, it entrenches corporate control over labour markets, sidelining Indigenous, disability justice, and communal hiring models that prioritise collective well-being. Historical precedents—from eugenics-era hiring tests to modern algorithmic bias—reveal a pattern of 'neutral' tools masking systemic exclusion. True reform requires dismantling the extractive logic of corporate recruitment, centring worker agency through cooperatives, transparent audits, and community-governed networks. Without these shifts, AI will remain a tool of neoliberal labour control, not liberation.