By name: aaai-topic-selection description: Use when deciding whether a project is a strong AAAI submission across its broad AI scope, should be reframed or routed to a dedicated track such as AI for Social Impact or AI Alignment, or should instead go to IJCAI, NeurIPS, ICML, ICLR, AISTATS, UAI, ACL, CVPR, KDD, CHI, ICRA, or another specialist venue.
AAAI Topic Selection
Use this while the project is still movable. AAAI is broad across artificial intelligence, so a strong submission should make an AI contribution that is intelligible beyond a narrow subfield.
Strong AAAI signals
- Clear AI problem and contribution: method, theory, system, benchmark, dataset, evaluation, social impact, alignment, human-AI interaction, planning, reasoning, learning, NLP, vision, robotics, or knowledge representation.
- Evidence that supports a general AI claim, not only a local application result.
- Responsible treatment of ethics, safety, privacy, fairness, social impact, or misuse when the paper touches those areas.
- Reproducibility path strong enough for checklist scrutiny.
- Narrative clear enough for Phase 1 reviewers from adjacent AI areas.
Weak AAAI signals
- Pure application deployment with little AI insight.
- Benchmark bump without mechanism, analysis, or robust comparison.
- Closed system with no reviewable evidence.
- Paper better framed as statistics, NLP, vision, HCI, robotics, or systems for a specialist venue.
- Policy-sensitive claims with thin ethics or stakeholder analysis.
Routing logic
- Prefer IJCAI for broad AI work with an international AI community emphasis.
- Prefer NeurIPS, ICML, or ICLR for stronger ML method/theory or representation-learning framing.
- Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis.
- Prefer ACL, CVPR, KDD, CHI, ICRA, or systems venues when the contribution is domain-specific.
- Prefer a workshop if evidence is preliminary but the idea is timely.
Fit-versus-route table
AAAI's breadth is an asset only when the contribution reads as general AI, not a narrow benchmark result. Use the dominant signal to decide between AAAI and a specialist venue.
| Project shape | AAAI fit | Better route if not |
|---|---|---|
| New planning or KR mechanism | strong, core AAAI turf | UAI for pure uncertainty |
| ML method with broad insight | plausible | NeurIPS/ICML for deep theory |
| Domain deployment, thin AI | weak | KDD, CHI, or ICRA |
| Stakeholder-facing impact work | strong via AI for Social Impact | domain policy venue |
Worked vignette
A team has a fairness-aware allocation system for a city service. The AI insight is a constraint formulation, and the stakes are social. Walking the signals: the contribution generalizes beyond the one city (strong signal) and is policy-sensitive (needs stakeholder evidence). Verdict: AAAI fit is strong, routed to AI for Social Impact rather than the Main Track, with harm and stakeholder analysis treated as required evidence, not an afterthought.
Output format
[AAAI fit] strong / plausible / weak / no
[Track route] Main / AI for Social Impact / AI Alignment / other
[Core AI contribution] <one sentence>
[Evidence required] <experiment, theory, artifact, stakeholder analysis>
[Best venue route] AAAI / IJCAI / NeurIPS / ICML / ICLR / AISTATS / UAI / domain venue