| name | facct-reproducibility |
| description | 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. |
FAccT Reproducibility
Use this before submission and again before camera-ready. At FAccT, transparency is not only the
subject of the field — it is a norm the community holds its own papers to. But FAccT
reproducibility is broader than "does the code run": it spans releasing and documenting the data
and models behind an audit, making a qualitative study auditable without exposing participants,
and being honest where confidentiality or proprietary access genuinely bars release. The goal is
that a competent reader could trace how you got from evidence to conclusion — and judge whether the
harm you claim is real.
Transparency map
- Map each finding to a verifiable location — a paper section, a table generated from released
analysis, a codebook, or a documented case record.
- For quantitative audits: release the analysis code, the dataset (or documented access), the
exact metrics and subgroup definitions, and the seeds/versions — enough that a reader could
re-run the disaggregation and reach your gaps.
- For qualitative/participatory work: release what can be shared safely — the interview
protocol, the codebook, aggregate coded results, consent materials — and state clearly what
cannot be shared and why (participant confidentiality, community agreement).
- Document datasets and models, not just release them. A datasheet for a dataset, a model
card for a model, and a data statement for a language corpus are the FAccT-native
documentation genres; use them to record provenance, composition, intended use, and known limits.
- Keep the paper and artifact consistent. A disparity in the PDF that no released analysis
reproduces is the contradiction reviewers read as carelessness — or worse, as an unfalsifiable
harm claim.
Documentation-and-availability audit
| Claim in the paper | Weak availability answer | FAccT-ready answer |
|---|
| "We audit N deployed systems" | "Data available on request" | Released dataset (or documented access) + analysis code + subgroup definitions |
| "Our dataset is representative" | Raw files with no context | A datasheet: how collected, who is in it, gaps, intended and off-label uses |
| "Our model behaves fairly" | Weights only | A model card: evaluation disaggregated by group, intended use, known failure groups |
| "We interviewed P affected people" | Nothing (privacy cited vaguely) | Protocol + codebook + aggregate results + a clear, specific confidentiality boundary |
| "The LLM produced these outputs" | "We used a chatbot" | Model IDs and dates, prompts, cached raw outputs, sampling settings |
"Available on request" reads as not available; convert every such line into a concrete release,
proper documentation, or an explicit, justified exception.
Provenance pinning
[Scraped/mined data] record source, extraction date, and terms; archive the extracted dataset,
not just the scraper; document deduplication and filtering
[Protected attributes] document how group labels were obtained/inferred and their error
[Models] record exact model identifiers + access dates; cache raw prompts and outputs;
report sampling settings; a live-API-only study re-samples, it does not reproduce
[Qualitative] version the codebook; log coding decisions; keep an audit trail a second
reader could follow
[Consent] keep the consent/ethics record aligned with what you release
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each disaggregated table/figure from released
data.
- Scripted: analysis scripts exist but need documented manual steps or restricted-data access.
- Documented: for qualitative or confidential work, the protocol, codebook, and aggregate
results let a reader audit the reasoning without re-running.
For FAccT, aim turnkey for anything a reviewer could rerun quickly (a fairness-metric recomputation,
a plot from released results); confidential interview data or proprietary system access stays
documented with the boundary stated. Stating the achieved level honestly beats promising turnkey
behavior that fails.
Vignette: a mixed-methods accountability study
Consider a study combining a quantitative audit of a benefits system with interviews of claimants.
Its transparency spine: the audit code with pinned data versions and subgroup definitions; the
released (or access-documented) audit dataset with a datasheet; the interview protocol, codebook,
and aggregate themes; the consent and ethics record; and one honest paragraph on what cannot be
shared (claimant identities, the agency's internal data) and why — so the audit is falsifiable and
the qualitative reasoning is auditable, without re-harming participants.
Consistency and camera-ready pass
- Before submission: every disparity/finding traces to released or documented evidence; datasheets
and model cards drafted; the artifact is anonymized (no author names, institution paths, or
identity-revealing repository).
- Before camera-ready: swap any anonymized link for a permanent one, finalize the datasheet/model
card, and align the Ethical Considerations and Adverse Impacts statements with what you release.
Output format
[Finding inventory] <finding -> evidence location>
[Availability] concrete release / documented access / vague / missing
[Documentation] <datasheet / model card / data statement present where relevant? yes/no>
[Provenance gaps] <scrape terms / proxy labels / model caching / codebook>
[Reproducibility level] turnkey / scripted / documented, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload, kept anonymous>
