FAccT Skills

Use when planning an ACM FAccT project timeline from venue fit through abstract registration, OpenReview submission, the rebuttal window, the new Accept/Revise/Reject revise-and-resubmit round, two-round camera-ready, and in-person presentation, with backward-planning offsets for an interdisciplinary responsible-AI paper and honest handling of the single annual deadline.

Use when auditing an ACM FAccT submission for OpenReview readiness — the mandatory abstract-registration gate with focus-area selection, the acmart anonymous/review PDF, the 14-page (+1 endmatter) budget, mutual anonymity, the required Generative AI Usage Statement and optional ethics/adverse-impacts statements, archival vs non-archival choice, and desk-reject triage before the deadline.

Use when designing or auditing ACM FAccT empirical work — quantitative fairness audits with disaggregated metrics and fair baselines, qualitative and participatory studies with coding and reflexivity, mixed-methods designs, sound handling of protected attributes and proxies, consent and IRB for human-subjects and community-facing work, and matching evidence to the shape of a fairness/accountability/transparency claim.

Use when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart sigconf proceedings format, ACM metadata (DOI, ORCID, CCS concepts, rights), the two-round camera-ready calendar for Accept vs Revise papers, ACM Open Access, finalizing datasheets/model cards, and integrating required changes without scope creep.

Use when positioning an ACM FAccT submission across its many disciplinary lanes — algorithmic fairness/ML, HCI, law and policy, STS and critical theory, and prior FAccT/FAT* proceedings — writing delta-first contrast that a mixed reviewer pool will accept, citing borrowed constructs to their real origin, keeping self-citations mutually anonymous, and declaring overlap with workshops, preprints, and prior versions.

Use when deciding what belongs in an ACM FAccT paper body versus its endmatter statements, appendices, and supplementary material — the 14-page acmart budget, the extra endmatter page for ethics/adverse-impacts statements, the rule that decision-critical evidence stays in the reviewed pages, mutually-anonymous supplementary material, and how to split an interdisciplinary paper between body, endmatter, and artifact.

Use when revising an ACM FAccT paper so that a fairness/accountability/transparency contribution is legible on the first page to a mixed CS+law+social-science reviewer pool, claims are scoped to who is affected, limitations and harms are argued rather than recited, the interdisciplinary framing is native, and the acmart page budget and endmatter statements are used well.

Use when reasoning about how an ACM FAccT submission is evaluated — mutually-anonymous review by a mixed CS+law+social-science pool matched via author-selected focus areas, Area Chairs, the short factual-correction rebuttal, the new Accept/Revise/Reject decision with a revise-and-resubmit round, and how FAccT's interdisciplinary process differs from a pure-ML conference's single-shot rebuttal.

Use when drafting ACM FAccT author responses — the short factual-correction rebuttal after preliminary reviews, and, distinctively for the 2026-new process, the Revise-and-resubmit round where you address Area-Chair-prioritized concerns from a mixed CS+law+social-science panel, map each to a concrete change, and survive a re-review while keeping mutual anonymity.

Use when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets, model cards, and data statements; making qualitative and participatory work auditable without breaking confidentiality; pinning provenance for scraped and model-generated data; and keeping the paper, the supplementary material, and any released artifact consistent.

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.

Use when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment documentation, and released code/data — since FAccT's culture centers documentation and accountability infrastructure rather than a formal ACM artifact-badging track; covers what makes each genre credible, anonymized-review versus public-release versions, and consistency with the paper's harm claims.