Opportunity summary
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Evidence0 refs | 3 sources | 50% coverage
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ARXIV:2606.06081 · UNCATEGORIZED · SUBMITTED 06 JUN · 03:19 UTC · FRESHNESS FRESH
ARXIV:2606.06081UNCATEGORIZEDSUBMITTED 06 JUN · 03:19 UTCFRESHNESS FRESHRanjan Mishra · Jakob Schoeffer · arXiv
ScienceToStartup currently rates this 0.0/10 on the public viability pass. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human…
Opportunity summary
Pain customer pain not on file
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusively on point predictions as AI advice.
ScienceToStartup currently rates this 0.0/10 on the public viability pass. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human decision making. Code availability…
Uncategorized moved forward this cycle; last verified June 2026. Public score 0.0/10. Production flags indicate code availability.
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ScienceToStartup currently rates this 0.0/10 on the public viability pass. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human…
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10.48550/arXiv.2606.06081Abstract
Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusively on point predictions as AI advice. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human decision making. In this paper, we develop the first formal framework for measuring appropriate reliance on set-valued AI advice within the sequential judge-advisor paradigm, spanning both classification and regression tasks. For classification, we first introduce the dimensions that are necessary for evaluating set-valued AI advice. We then define two metrics: correct reliance rate on AI and correct reliance rate on self, which jointly characterize appropriate reliance in this setting. For regression, we introduce quantity of AI reliance and quality of AI reliance, which respectively measure whether a decision maker utilized the AI advice and whether their reliance helped them get closer to the ground truth relative to their initial estimate. Through the application of our framework, we demonstrate how these metrics capture important nuances in human-AI collaboration that existing measures overlook.
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Proof status
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PROBLEM
Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
METHOD
Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusively on point predictions as AI advice.
RESULT
ScienceToStartup currently rates this 0.0/10 on the public viability pass. However, set-valued AI advice (e.g., discrete sets or continuous intervals) is increasingly being used to communicate uncertainty and improve human decision making. Code availability is flagged in the pro...
WHY NOW
Uncategorized moved forward this cycle; last verified June 2026. Public score 0.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 13, "author": "Ranjan Mishra; Jakob Schoeffer", "title": "A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice", "creation date": null
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partial
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Build Passport
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status
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reason
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proof status
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confidence low
next verification path
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Classify regulatory flags before commercialization planning.
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Prototype owner missing.
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Operator workflow not sourced.
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Regulatory need unclassified.
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ARTIFACTS
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