RESEARCH TOPIC / DECISION-SCIENCE
Uncertainty, causality, forecasting, and human judgment.
GROUNDING MAP
Explore 15 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
LLMs can shift answers toward a user's stance or pushback, and ratings miss it. Investment reviews should not count AI agreement as independent verification, should re-verify AI objections, and should probe how models update at rollout and on change.
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Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.
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Compare the existing and AI-assisted workflows under aligned conditions, including human review and revision time, confirmed quality, and unfinished cases. Use a record to decide deployment, hold, retest, or stop.
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We design procedures that do not equate fairness with merely displaying explanations, but instead enable individuals to understand, present their circumstances, correct data, and proceed to human review and relief.
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Based on experiments showing that avoidance after observing algorithmic errors and acceptance with minor correction rights vary, we design operations that are neither mandatory use nor laissez-faire.
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Rather than prompting guesses solely based on accuracy, design the boundaries between answering, confirming, and stopping by accounting for semantic uncertainty, unanswerable questions, and the losses from incorrect answers versus abstention.
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Personalized conversational AI can correct misconceptions or steer users toward commercial interests without their awareness. We define operational conditions that distinguish legitimate behavioral support from manipulation and show how to measure autonomy.
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Whether AI explanations, confidence displays, judgment order, and user control can truly reconcile the adoption of correct advice with the rejection of incorrect advice is examined by organizing conditions from behavioral experiments.
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Rather than framing predictive ability as individual talent, we consider practical conditions derived from probabilistic expression, brief training, aggregation of independent judgments, and continuous scoring and re-analysis.
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Begin causal inference from observational data not by choosing methods, but by designing comparisons; clarify the roles and limitations of target trials, propensity scores, and regression discontinuity.
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By tracking data quality not only in terms of accuracy but also meaning, proxy indicators, and on-site correction pathways, we identify where numbers lead to judgment errors.
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We organize methods for auditing fairness without reducing it to a single score, by examining the usage context, harm, types of errors, intersecting populations, and post-deployment changes.
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A meta-analysis of 106 experiments and management studies helps distinguish extension—outperforming humans alone—from true synergy, where collaboration exceeds the best human or AI performance, and identifies when collaboration is viable.
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We synthesize results from randomized trials and clinical studies involving physicians to clarify the conditions under which generative AI advice improves judgment, and why mere access does not yield effects.
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Data volume and decision quality are not the same. We examine the conditions for a usable data foundation, considering metric meaning, causality versus correlation, update frequency, and on-the-ground context.
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