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cs.LG2026
An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
Maja Pavlovic, Silviu Paun, Massimo Poesio
Central to human-aligned AI is understanding the benefits of human-elicited labels over synthetic alternatives. While human soft-labels improve calibration by capturing uncertainty…
cs.HC2026
Toward Human-AI Complementarity Across Diverse Tasks
Yuzheng Xu, Annya Dahmani, Matthew D. Blanchard +13
Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. How…