Confidence-Building Measures for Artificial Intelligence: Workshop proceedings
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arXiv:2603. 29693v3 Announce Type: replace Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks.
DualStake introduces a dual-path confidence calibration for deep research agents, adding step confidence elicitation after each retrieval step. The method shows that evidence confidence (E-Conf) after the final retrieval provides a stronger uncertainty signal than answer confidence (A-Conf), and that A-Conf is largely influenced by E-Conf. By applying margin‑clipped, confidence‑dependent stake rewards, DualStake aligns both E-Conf and A-Conf with answer correctness, improving calibration across multiple QA benchmarks without harming accuracy.
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv:2606. 06081v1 Announce Type: new Abstract: Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
The article discusses how artificial intelligence is reshaping measurement in economics by converting unstructured data into structured variables at low cost, enabling large‑scale measurement that was previously infeasible. It outlines three stages—discovery, construct definition, and observation—where AI impacts the measurement pipeline and stresses the importance of rigorous validation to ensure credible inference. The review offers guidance on navigating the shift from a single scalable measure to multiple plausible ones that can lead to differing empirical conclusions.
arXiv:2607. 06656v1 Announce Type: new Abstract: Machine learning models are often intended to augment rather than replace human decision makers, by providing information that is complementary to human judgement.