INSIGHTS / RESEARCH NOTES
We document research on AI, data, behavioral science, and organizations, including methods worth testing in practice. Each note shares not only conclusions, but also hypotheses, observations, and limitations.
69 RESEARCH NOTES
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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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Conversation continuity and lasting real-world behavior are distinct. Drawing on meta-analyses and field RCTs of conversational coaches, this article outlines evaluation designs that avoid mistaking short-term responses for behavior change.
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We do not leave AI privacy measures solely to post-collection anonymization; we reduce necessity, retention, duplication, learning, logging, and access across the entire lifecycle.
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Monitoring only changes in input distribution does not reveal actual harm; design multi-layered monitoring and response procedures that connect data, predictions, decisions, and delayed outcomes.
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The mere format of artificial generation does not guarantee safety; we address attackers, memorization, differential privacy, and utility by use case in a single evaluation table.
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We do not end the discussion with the notion that “metrics become targets and the frontline deteriorates”; instead, we distinguish bias toward easily measurable work, gaming, and actual improvement to design robust operations that resist breakdown.
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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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RAG accuracy is determined not only by the model but also by how quickly document version, expiration, source, permissions, and deletion are reflected in the index; we consider operational design from knowledge benchmarks.
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Filtering suspicious text alone cannot protect AI that reads external data and executes tools; we organize multi-layered defense through attack evaluation, structured input, and official classification.
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