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 |
| "We improve transparency." | "we surface |
| "The model is accurate on a large dataset." | "accuracy disaggregated by |
| "This raises ethical concerns." | "this risks |
| "Prior work is limited." | " |
[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>