WorkRB is the first open community-driven benchmark for AI in the work domain, organizing 13 tasks from 7 groups with dynamic multilingual ontology loading and modular design for proprietary task integration.
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UNVERDICTED 3representative citing papers
A gradient-based algorithm learns feature representations to enable efficient post-hoc fairness-accuracy trade-offs in classification without retraining.
Secondary bounded rationality describes how AI recruitment algorithms reproduce structural inequality by optimizing for biased proxies of competence drawn from cultural and social capital disparities.
citing papers explorer
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WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain
WorkRB is the first open community-driven benchmark for AI in the work domain, organizing 13 tasks from 7 groups with dynamic multilingual ontology loading and modular design for proprietary task integration.
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Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off
A gradient-based algorithm learns feature representations to enable efficient post-hoc fairness-accuracy trade-offs in classification without retraining.
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Secondary Bounded Rationality: A Theory of How Algorithms Reproduce Structural Inequality in AI Hiring
Secondary bounded rationality describes how AI recruitment algorithms reproduce structural inequality by optimizing for biased proxies of competence drawn from cultural and social capital disparities.