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

Build a participant interaction study

Randomly assign people to deliberative or non-deliberative AI interactions and measure what changes afterward.

The basic idea

A participant study joins an interaction protocol to a questionnaire and an assignment rule. The intervention should differ only on the intended treatment dimension, while prompts, time, model access, and measurement remain comparable across arms.

Example

Randomly assign participants to discuss a policy question with either a deliberating council or a matched reconsideration condition, then compare post-interaction beliefs and decision confidence.

How to do it

  1. 1

    Create the experimental arms and verify the information each participant and model can see.

  2. 2

    Choose participant-level random assignment, allocation ratios, blocks if prespecified, and a reproducible seed.

  3. 3

    Add consent language, the interaction prompt, transition text, and the post-interaction questionnaire.

  4. 4

    Preview every arm, launch a small pipeline test, and inspect transcripts, assignment, completion, and exports before recruitment.

What to watch for

  • A condition should not receive extra time or model effort unless that difference is part of the treatment.
  • Do not expose hidden condition labels to participants.
  • Institutional review, recruitment rules, and participant compensation remain the researcher's responsibility.

What a useful result looks like

A complete study links each consented participant, assigned condition, preserved transcript, questionnaire response, and treatment-exposure record without revealing private links or hidden assignments.