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
1 / 6 PAGE
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.
Read article
Design evidence for AI-agent actions around a chain of approval, execution, and state change—not full-content retention. Capture disputed content selectively under protection, then test reconstructability and minimization as separate release gates.
Read article
A role-and-output reading of EU AI Act Article 50, translating human-facing disclosure, machine-readable marking, exceptions, and version evidence into one placement record.
Read article
Instead of reusing a human session, separate the principal from the agent and evaluate a delegation that binds resource, action, audience and lifetime at execution time.
Read article
After AI-enabled RPA was introduced, interviews suggested monthly research time fell about 90%. Later, only 10–20% of estimated freed time was explained as new work or extra output. This note shows how to measure the time actually freed and where it went.
Read article
After work has been redesigned, decide how remaining steps combine core systems, APIs, RPA, AI, and people—and what proves the operation can run without its builder.
Read article
Treat faster local tasks as only one signal; redesign bottlenecks, exceptions, expert handoffs, and the allocation of released capacity across the full workflow.
Read article
Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.
Read article
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.
Read article
Based on evidence that button emphasis, obstruction, defaults, and repeated requests alter choices, audit the procedures and visibility of acceptance, refusal, and withdrawal symmetrically.
Read article
Do not mistake general research on psychological safety for AI-specific causal evidence; integrate dissent, error reporting, stop proposals, and retaliation checks into the execution procedures for AI implementation.
Read article
Given habituation to repeated warnings, false alarms, and ignoring due to irrelevant notifications, design a warning budget by risk, display variations, and mandatory stops.
Read article
