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facct-writing-style【数据与研究】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 stat…
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Use this when revising the main paper. FAccT papers are read by a deliberately mixed panel — a computer scientist, a lawyer, and a qualitative social scientist may each land on your paper — so the writing must make a fairness, accountability, or transparency contribution legible to all three and must be honest about who is affected and how. The failure this skill prevents is a paper that is excellent inside one discipline and illegible or naive to the others.
| Section | Job it must do | Common failure |
|---|---|---|
| Intro | Sociotechnical problem, who is affected, contribution, what changes — first page | Leads with a model/benchmark, not a harm or accountability question |
| Background / motivation | Why this matters now, grounded across the relevant disciplines | Motivation by assertion; one-discipline framing |
| Approach / method / argument | The technique, study design + protocol, or the conceptual/legal argument | Method too thin to re-run, or critique with no engagement with the artifact |
| Findings / evaluation | Disaggregated results, coded themes, or a defended argument | Aggregate-only numbers that hide subgroup harm |
| Limitations & adverse impacts | The harms and threats that actually bite, each bounded | Generic list untethered from this work |
| Related work | Cross-disciplinary, delta-first positioning | Cites only one lane; misses the obvious neighbor in another field |
| Draft pattern | FAccT-safe rewrite |
|---|---|
| "Our method is fair." | "our method equalizes across on , within ; residual gaps in §5" |
| "We improve transparency." | "we surface to for , and we test whether they can act on it" |
| "The model is accurate on a large dataset." | "accuracy disaggregated by (Table 2); the largest error falls on " |
| "This raises ethical concerns." | "this risks to ; we mitigate by , residual risk in the Adverse Impacts statement" |
| "Prior work is limited." | " measured X but not the deployment outcome Y that governs harm; we measure Y" |
[Construct] does the fairness/transparency metric mean what you claim for the affected group?
[Population] which groups are in and out of scope; who is invisible in your data?
[Deployment] what goes wrong if this ships or is cited as license to deploy? who is hurt?
[Reflexivity] whose standpoint shaped the framing? (state it in Positionality upon acceptance)
-> for each that bites: state it, then state the mitigation, next to the affected result
A draft auditing a commercial system, with aggregate accuracy up front and a short ethics paragraph at the end: move the disaggregated subgroup results to the first result table, put the worst-affected group in the abstract, argue the construct-validity threat (does your proxy for the protected attribute mean what you claim?) beside that table, and move method minutiae to supplementary. The test of a good cut: a mixed reviewer can answer "who is harmed, by how much, and how sure are we?" from the body alone.
[Writing diagnosis] clear / under-motivated / over-claimed / single-discipline / harm-blind / over-scoped
[First-page fix] <new framing leading with the FAccT contribution and who is affected>
[Claim audit] <claim -> population + construct + operationalization -> scoped? yes/no>
[Harm fix] <adverse impact that bites -> mitigation to add, placed by the result>
[Anonymity edits] <system names / self-citations / positionality kept out of the anonymous PDF>