# Facct Artifact Evaluation > 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. - Skill: `thedixitjain/facct-artifact-evaluation` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/facct-artifact-evaluation` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/facct-artifact-evaluation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/facct-artifact-evaluation --- # FAccT Artifact Evaluation Use this for the material that backs a FAccT paper's transparency and accountability claims. Note the venue difference up front: FAccT does **not** run the SIGSOFT-style ACM Artifact Review and Badging track that software-engineering venues use, and it does not hand out Available/Functional/ Reusable/Reproduced badges. **待核实**: confirm on the current Author Guide whether any optional artifact/reproducibility appendix or badge scheme has been added for your cycle. What FAccT *does* have is a strong norm of **accountability documentation** — datasheets, model cards, data statements, audit trails, and impact assessments — plus released code and data. Treat those genres as your artifact and make each one credible on its own. ## The FAccT documentation genres (know which your paper needs) | Genre | What it documents | When your paper needs it | |---|---|---| | Datasheet for a dataset | Motivation, composition, collection, preprocessing, uses, distribution, maintenance | You release or rely on a dataset | | Model card | Intended use, training data, evaluation **disaggregated by group**, ethical considerations, limits | You release or audit a model | | Data statement (for language data) | Speaker/annotator demographics, curation rationale, language variety | You build or study a text/NLP corpus | | Audit / evaluation report | Method, subgroup metrics, thresholds, what was and was not tested | Your contribution is an audit | | Impact / risk assessment | Foreseeable harms, affected populations, mitigations, residual risk | Deployment or dual-use is plausible | Pick the genres your claims actually require; a model audit with no model card, or a dataset paper with no datasheet, reads as incomplete to this community. ## What a credible documentation artifact contains ```text [Provenance] where the data/model came from, when, under what terms and consent [Composition] who/what is in it, who is absent, and the resulting blind spots [Disaggregation] evaluation broken out by protected/affected subgroup, with uncertainty [Intended use] what it is for — and an explicit "off-label" / do-not-use list [Limits & harms] known failure groups and foreseeable adverse impacts, not just accuracy [Maintenance] who updates it, how issues are reported, how long it persists [License] a clear license for released code/data so others can lawfully reuse it ``` ## Released code and data (the reproducibility half) - Ship the analysis that turns data into the paper's disaggregated findings, with pinned data versions and seeds, so a reader can re-run the harm claim. - Deposit released data or a public archive in a persistent location (e.g. a DOI-issuing repository) for the camera-ready; keep it consistent with the datasheet. - For model-generated or scraped inputs, cache raw outputs and record model IDs, dates, and terms — a study that needs a live API or a since-changed website re-samples rather than reproduces. ## Anonymized review version vs. public release - **At submission:** any documentation or code shipped for reviewers must be **anonymized** — no author names, institution paths, cluster URLs, or identity-revealing repositories, and the Positionality statement stays out entirely (it is not anonymous). - **After acceptance:** replace anonymized placeholders with the public, licensed, persistently archived versions the camera-ready cites, and finalize the datasheet/model card so it matches the released artifact exactly. ## Consistency with the paper's harm claims The artifact's job at FAccT is to make the paper's **accountability claims checkable**. Every disparity, harm, or transparency benefit the paper asserts should be traceable into the documentation or released analysis. A model card whose disaggregated numbers disagree with the paper's table, or an impact assessment that omits the harm a reviewer can foresee, undercuts the paper more than having no artifact at all. ## Vignette: an audit paper's artifact set A paper auditing a commercial classifier ships: a **datasheet** for the evaluation dataset (how assembled, subgroup composition, consent basis); a **model card**-style report for the audited system as the authors understand it (intended use, disaggregated error, failure groups); the **audit code** with pinned data and seeds regenerating each subgroup table; and a short **impact assessment** naming who is harmed by both the system and by publishing the audit, with mitigations. All anonymized for review, all public and licensed at camera-ready, all consistent with the paper's tables. ## Calibration - FAccT's artifact expectations are **documentation- and release-centered**, not badge-centered; do not import a Docker-image/badge checklist as if it were the bar. - Whether any optional artifact appendix, reproducibility checklist, or badge exists is cycle-volatile — confirm on the current Author Guide (**待核实**). ## Output format ```text [Genres needed] [Artifact role] anonymized review version / public release [Contents] [Claim mapping] where in the documentation/analysis it is checkable? yes/no> [Consistency] [Fixes before upload] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `FAccT-Skills/skills/facct-artifact-evaluation/SKILL.md`