AI Deliberator
Guides
How to use AI DeliberatorView all 58 guides

Getting started

Evaluate and export a Council runHow to ask a better decision questionHow to choose models for an AI councilRun your first AI councilUse the Consideration MapContinue a decision with follow-up questionsEstimate and control LLM costs

Everyday decisions

Use an AI council for a career changeEvaluate a leadership role or promotionThink through a relationship decisionDecide whether to move to a new cityCompare degrees, certificates, and education optionsAnalyze a major purchaseUse live evidence mode

Work and teams

Use deliberation for a business strategy decisionSupport a hiring decision responsiblyPrioritize a product roadmapRun a decision pre-mortemMap a stakeholder disagreementAnalyze a policy proposal

Research design

Duplicate and pilot an experimentDesign an LLM deliberation experimentDefine outcomes and dependent variablesConfigure experimental conditions and independent variablesCompare deliberation with homogeneous reconsiderationBuild an independent model ensembleUse randomization, replications, and compute matching

Participant studies

Build a participant interaction studyDesign pre- and post-interaction questionnairesChoose Likert, slider, and questionnaire formatsAdd attention, manipulation, and exposure checksAnalyze and export participant-study resultsBuild a composable experiment timelineMeasure human–AI advice relianceAdd process tracing and cognitive tasksRun a human deliberation conditionBuild a human–AI hybrid deliberation chainRun a text, image, or video stimulus experimentRun a learning and tutoring experimentPlan a powered participant studyMonitor recruitment, cost, and attrition

Behavioral games

Design a behavioral economics gameRun a Dictator GameRun an Ultimatum GameRun a Trust or Investment GameRun a Prisoner’s DilemmaRun a Public Goods GameRun a Stag Hunt GameConfigure game payoffs, incentives, and feedbackManage private game links, consent, and completion

Benchmarks and results

Select benchmark questions and subsetsRun MMLU-Pro in AI DeliberatorRun PolicyBench for public-policy reasoningRun CogToM for social cognitionCreate and read 95% confidence-interval plotsCollaborate and preserve a reproducible experiment recordAnalyze sentiment and quantitative text metricsCompare two Council runs
Workspace

Participant studies

Analyze and export participant-study results

Use complete summaries and structured exports for outcomes, change scores, checks, assignments, and transcripts.

The basic idea

Analysis should follow the assignment unit and measurement timing. Report arm sizes, completion and attrition, distributions, uncertainty, missingness, treatment exposure, and the prespecified effect—not only a difference in post-treatment averages.

Example

Compare pre/post change in policy support across conditions, show 95% confidence intervals, and report both intention-to-treat and prespecified exposure-sensitive estimates.

How to do it

  1. 1

    Export the data and preserve the original study, protocol, outcome, and questionnaire versions.

  2. 2

    Verify arm counts, duplicate participants, timing, missing responses, attrition, and exposure diagnostics.

  3. 3

    Calculate descriptive statistics and the prespecified contrast with uncertainty appropriate to the assignment.

  4. 4

    Link reported results back to the exact questionnaire items and include sensitivity analyses without replacing the primary result.

What to watch for

  • Completed-only comparisons can break randomization.
  • Multiple outcomes increase false-positive risk unless priorities or adjustments are prespecified.
  • Transcripts may contain sensitive participant text and require controlled handling.

What a useful result looks like

The export supports reproducible arm comparisons while retaining enough context to audit exclusions, treatment delivery, and questionnaire interpretation.