facct-topic-selection
Use when deciding whether a responsible-AI project belongs at ACM FAccT or should route to a pure-ML venue (NeurIPS/ICML/ICLR), an HCI venue (CHI/CSCW), a law/policy venue, or an AI-ethics venue (AIES), by testing whether fairness, accountability, or transparency is a first-class contribution and whether the interdisciplinary framing is native rather than bolted on.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# FAccT Topic Selection Decide the venue before drafting. **ACM FAccT — the Conference on Fairness, Accountability, and Transparency** — is the flagship *interdisciplinary* responsible-AI venue. Its reviewer pool spans computer science, law, the social sciences, the humanities, and policy, and its defining demand is that **fairness, accountability, or transparency (FAccT) is a first-class contribution**, not a fairness paragraph appended to a systems result. A technically strong paper whose real center is a new model, a new interaction technique, or a doctrinal legal argument — with FAccT concerns merely gestured at — is respected and then rejected as out of scope. ## The routing question that matters most The decisive question is rarely "does this touch fairness/AI?" but **"is a fairness, accountability, or transparency question the actual contribution, and is the sociotechnical framing native?"** FAccT uniquely rewards work that takes the *social* and the *technical* as inseparable. A paper that would lose nothing if you deleted the equity framing belongs elsewhere; a paper whose whole point is who is harmed, who is accountable, or what can be made legible belongs here. ## Siblin
- The routing question that matters most
- Sibling-venue routing table
- Contribution shapes FAccT rewards
- The two sharpening tests
- Interdisciplinary rigor, not interdisciplinary gesture
- Cheap reconnaissance before committing
- Decision procedure
What does the facct-topic-selection skill do?
Use when deciding whether a responsible-AI project belongs at ACM FAccT or should route to a pure-ML venue (NeurIPS/ICML/ICLR), an HCI venue (CHI/CSCW), a law/policy venue, or an AI-ethics venue (AIES), by testing whether fairness, accountability, or transparency is a first-class contribution and whether the interdisciplinary framing is native rather than bolted on.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-topic-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.