--- name: AAAI Experiments slug: aaai-experiments category: Quality description: A systematic audit for AAAI submissions to ensure empirical evidence supports AI contributions. Covers baselines, ablations, reproducibility, human evaluation, and alignment claims. github: "https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-experiments" language: Stata stars: 875 forks: 106 install: "git clone https://github.com/brycewang-stanford/Awesome-Journal-Skills" added: 2026-07-20T06:49:35.635Z last_synced: 2026-07-28T07:01:22.031Z canonical_url: "https://dirskills.com/skills/aaai-experiments" --- # AAAI Experiments A systematic audit for AAAI submissions to ensure empirical evidence supports AI contributions. Covers baselines, ablations, reproducibility, human evaluation, and alignment claims. **Install:** `git clone https://github.com/brycewang-stanford/Awesome-Journal-Skills` ## README # 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. | Manuscript claim | Required evidence | Phase-1 risk if missing | Checklist hook | | --- | --- | --- | --- | | New AI capability | benchmark + qualitative failure cases | broad reviewer sees only engineering | datasets, metrics, baselines | | Better mechanism | single-factor ablations | gain looks like tuning luck | ablation and hyperparameter answers | | Robust deployment | shift / seed / subgroup stress test | result seems brittle | variance, compute, environment | | Social-impact or safety claim | stakeholder, harm, and misuse analysis | ethical claim looks asserted | ethics, limitations, data access | 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. - The AI-assisted review pilot is non-decisional, but it may surface checklist mismatches; make result provenance, seeds, data splits, and limits machine-readable enough that a human SPC/AC can quickly audit them. ## Pre-rebuttal freeze rule Before submission, decide which experiments would be impossible to add later under AAAI's rebuttal constraints: missing baselines, missing seeds, missing supplement files, or missing reproducibility checklist answers. Treat those as **pre-submission blockers**, not rebuttal TODOs. The author response can explain and clarify submitted evidence; it should not depend on new results, URLs, or repaired supplementary files. ## Evidence triage table Because an AAAI reviewer from an adjacent subfield must trust your numbers quickly, classify each experimental block by how much weight it can bear and what would strengthen it. | Block | Carries the claim when | Reviewer doubt | Cheap reinforcement | | --- | --- | --- | --- | | Headline benchmark | beats tuned recent baselines | "lucky seed" | seeds, variance bars | | Ablation | isolates one mechanism | "joint removal" | single-factor toggles | | Robustness | holds across split/shift | "one setting" | extra split or perturbation | | Human eval | protocol is documented | "rater bias" | IRB note, inter-rater agreement | ## Common AAAI experiment rejects - Benchmark bump with no mechanism analysis, which a broad committee reads as engineering, not AI insight. - Baselines weaker than current open-source systems, so the comparison looks unfair. - A Social-Impact or alignment claim with no stakeholder, harm, or risk-mitigation evidence. - Results that rely on a closed API with no reproducible substitute for the checklist. ## Worked vignette A planning paper reports a single-seed win on one domain. Audit: the headline block "needs robustness" and "needs variance", so the fix before the deadline is five seeds with confidence intervals plus one extra IPC-style domain. Because new results cannot rescue this in rebuttal, the team runs both before submission and aligns the checklist's seed answer to the supplement. ## Output format ```text [Claim] [Evidence status] sufficient / needs baseline / needs ablation / needs robustness / unclear [Fairness issue] [Checklist dependency] [Pre-rebuttal blockers] [Fast fix] ```