Careful Judge: Safe and Efficient Human-AI Collaborative Decision Making
In human-AI collaborative decision making, human review can prevent unsafe AI decisions, but each human judgment is costly. Treating human intervention after AI abstention as a one-off fallback misses the opportunity to improve future AI decisions for greater automation, yet AI adaptively learning from selectively queried human feedback breaks safety guardrails calibrated for old models. We approach this challenge with CARE---calibrated adaptive rectification and escalation---an end-to-end pipeline that combines AI models and human reviewers to guarantee safe, human-aligned decisions, while continuously learning from human feedback to achieve greater automation with fewer human queries. C...
| Authors | Chenyu Zhang, Rachel Luo, Boyi Li et al. |
|---|---|
| Primary category | stat.ML |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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