aaai-topic-selection

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