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

Work and teams

Support a hiring decision responsibly

Structure job-related evidence and tradeoffs while keeping humans accountable and avoiding sensitive or protected information.

The basic idea

AI can help organize role requirements, evidence, interview gaps, and competing interpretations. It should not make autonomous employment decisions or infer protected traits, personality disorders, honesty, or future behavior from thin evidence.

Example

Compare candidates against a predefined rubric using job-related work samples and structured interview evidence, then ask what additional evidence would resolve uncertainty.

How to do it

  1. 1

    Create the job rubric before reviewing candidates and use the same criteria for everyone.

  2. 2

    Remove protected and unnecessary personal information from prompts.

  3. 3

    Ask the council to identify evidence for each criterion, contradictory evidence, and missing evidence.

  4. 4

    Keep the final decision with trained humans and document how AI output was checked.

What to watch for

  • Employment law and organizational policy vary; obtain qualified review.
  • Do not use demographic proxies or unvalidated psychological inferences.
  • Models can reproduce bias even when the prompt appears neutral.

What a useful result looks like

The council can improve the structure of human review, but it should never become an unaccountable hiring gate.