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 human–AI hybrid deliberation chain

Place one or more people between model turns to study mixed human–AI reasoning and influence.

The basic idea

A hybrid chain makes order and information exposure part of the treatment. A person placed after one model and before another can transmit, challenge, or redirect earlier reasoning, so seat order must be explicit and reproducible.

Example

Schedule OpenAI, DeepSeek, a human participant, and Gemini in sequence, with each seeing the selected prior messages and a separate chair producing the final synthesis.

How to do it

  1. 1

    Add human and model seats in the exact intended order.

  2. 2

    Specify for every turn whether it sees no history, the previous answer, selected messages, a summary, or the complete transcript.

  3. 3

    Define identity visibility, role instructions, deadlines, and what happens if the human does not respond.

  4. 4

    Pilot the entire chain and verify that each seat receives only its intended context.

What to watch for

  • Position effects can be mistaken for actor-type effects.
  • A human message can contain personal information or prompt-injection text and must be handled as untrusted input.
  • Model calls that wait for humans need durable, bounded resume behavior.

What a useful result looks like

The manifest and transcript show who contributed each turn, what information they received, and how the mixed chain produced its final outcome.