AAAI Experiments

AAAI Experiments

Use this before submission to ensure empirical evidence supports the AI contribution. AAAI reviewers may come from adjacent AI subfields, so experiments must be interpretable beyond one benchmark community.

Experiment audit

• Map every experimental block to a claim in the introduction.
• Compare against strong, recent, and fairly tuned baselines.
• Include ablations that isolate mechanisms rather than removing multiple components at once.
• Report uncertainty, variance, and statistical tests when small differences matter.
• Test robustness to data split, prompt, seed, environment, user population, or distribution shift when relevant.
• For human evaluation, document task, instructions, annotator pool, quality control, aggregation, and ethics/IRB status.
• Report compute, hardware, data access, model size, and training/inference cost.

Claim-to-evidence ledger

Build this table before adding new experiments. It keeps the AAAI evidence package aligned with the main text and with the reproducibility checklist.

For each row, mark ready / weak / missing and name the fastest fix that can be run before the supplementary-material deadline. Do not leave a claim in the abstract if its evidence row is weak.

AAAI-specific review pressure

• Phase 1 reviewers need a fast reason to trust the evidence.
• The reproducibility checklist must match the experiment descriptions.
• AI for Social Impact and AI Alignment claims require stronger treatment of stakeholders, harms, risk mitigation, and scope.
• New results usually cannot rescue the paper in rebuttal, so submit complete evidence upfront.