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facct-experiments

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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.

FAccT-Skills/skills/facct-experiments/SKILL.md

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# FAccT Experiments

Use this before submission when the empirical story is not yet locked. FAccT evidence is **not
leaderboard evidence**: the reviewer pool asks whether your study actually shows the *harm,
disparity, accountability gap, or transparency effect* you claim, on the *people* you claim it for,
with methods honest about their limits. The organizing principle is **evidence proportional to the
claim** — and because FAccT is interdisciplinary, "evidence" can be a disaggregated statistical
audit, a coded interview corpus, a participatory study, or a documented case, each held to its own
field's standard of rigor.

## Evaluation audit

- **Match evidence to the claim shape.** A claim about *disparate harm* needs **disaggregated**
  results by subgroup, not aggregate accuracy; a claim about *how affected people experience* a
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