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aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
AAAI-Skills/skills/aaai-experiments/SKILL.md
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# 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.