On the public citation graph
What can the Judge-Advisor System tell about appropriate reliance in human-AI interactions? measures and models of discrete decision-making
0 signed · 25 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/bdm.547openalex-snapshot
Aggregation of opinions based on correlated cues and advisors
inferredcites →https://doi.org/10.1007/978-1-4419-0742-4openalex-snapshot
Bayesian Item Response Modeling
inferredcites →https://doi.org/10.1007/s12144-022-03573-2openalex-snapshot
A meta-analysis of the weight of advice in decision-making
inferredcites →https://doi.org/10.1007/s42113-022-00157-yopenalex-snapshot
AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies
inferredcites →https://doi.org/10.1016/0030-5073(82)90237-9openalex-snapshot
External correspondence: Decompositions of the mean probability score
inferredcites →https://doi.org/10.1016/j.chb.2021.107018openalex-snapshot
Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice
inferredcites →https://doi.org/10.1016/j.jmp.2022.102695openalex-snapshot
Decline, adopt or compromise? A dual hurdle model for advice utilization
inferredcites →https://doi.org/10.1016/j.obhdp.2018.12.005openalex-snapshot
Algorithm appreciation: People prefer algorithmic to human judgment
inferredcites →https://doi.org/10.1037/0033-295x.100.3.432openalex-snapshot
Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment.
inferredcites →https://doi.org/10.1037/a0025648openalex-snapshot
The self-consistency model of subjective confidence.
inferredcites →https://doi.org/10.1038/s41467-024-51725-8openalex-snapshot
Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer
inferredcites →https://doi.org/10.1073/pnas.2111547119openalex-snapshot
Bayesian modeling of human–AI complementarity
inferredcites →https://doi.org/10.1073/pnas.2221311120openalex-snapshot
An astonishing regularity in student learning rate
inferredcites →https://doi.org/10.1109/tpami.2023.3331846openalex-snapshot
Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
inferredcites →https://doi.org/10.1111/j.1467-9280.2006.01704.xopenalex-snapshot
The Anchoring-and-Adjustment Heuristic
inferredcites →https://doi.org/10.1126/science.185.4157.1124openalex-snapshot
Judgment under Uncertainty: Heuristics and Biases
inferredcites →https://doi.org/10.1145/3485447.3512240openalex-snapshot
Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making
inferredcites →https://doi.org/10.1145/3593013.3594087openalex-snapshot
Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies
inferredcites →https://doi.org/10.1145/3594715openalex-snapshot
A Computational Model of Coupled Human Trust and Self-confidence Dynamics
inferredcites →https://doi.org/10.1145/3653708openalex-snapshot
Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making
inferredcites →https://doi.org/10.1145/3757601openalex-snapshot
Five Degrees of Separation: Investigating the Unexpected Potential of Displaced Human-AI Collaboration Protocols for Apter AI Support
inferredcites →https://doi.org/10.1146/annurev-psych-022423-032425openalex-snapshot
Metacognition and Confidence: A Review and Synthesis
inferredcites →https://doi.org/10.1287/mnsc.2020.3902openalex-snapshot
Combining Probability Forecasts: 60% and 60% Is 60%, but Likely and Likely Is Very Likely
inferredcites →https://doi.org/10.1609/aaai.v38i19.30082openalex-snapshot
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.