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 system needs qualitative or behavioral data from those people; a claim about accountability needs the institutional/process evidence, not a metric.
• Disaggregate, and defend the groups. Report metrics per protected/affected subgroup with uncertainty. State how group membership was defined and measured — proxies for race, gender, disability, or income carry construct-validity risk you must name and bound.
• Choose fair baselines and fair comparisons. For a fairness method, compare against the strongest prior fairness intervention and the do-nothing baseline, with an equal, documented budget; for an audit, compare against the vendor's own claims or a documented ground truth.
• Hold qualitative work to method. Coding schemes, inter-coder agreement where appropriate, saturation, an audit trail, and reflexivity about the researchers' standpoint — qualitative rigor is first-class at FAccT, not a soft option.
• Treat human subjects and communities with care. Document consent, IRB/ethics approval or its considered absence, compensation, data minimization, and how you avoid re-identifying or re-harming the people in your data — this is scored, and it feeds the Ethical Considerations statement.
• Design harms and limits in, not on. Know before you run which populations you cannot speak for and which disparities your instrument might miss, and instrument to surface them.

Claim-to-evidence design table

Handling protected attributes and proxies

[Definition]   state how each group is defined; whose categories are these, and who is erased by them?
[Measurement]  is the attribute observed, self-reported, or inferred? report proxy error and bias
[Intersection] test intersectional subgroups where numbers allow; note where they are too small
[Consent]      do the people classified know and agree? document the ethics basis
[Missingness]  who is absent from the data entirely, and how does that bound the claim?

Mixed-methods and participatory rigor

• Say why each method is present and what it does that the other cannot — triangulation, not decoration.
• For participatory or community-based work, document how the community shaped the questions, how findings return to them, and how power was handled — FAccT reviewers include people who do this seriously.
• Report negative and disconfirming evidence; a study that only confirms the authors' prior reads as advocacy, not research.

Vignette: auditing a hiring model

A paper claims a screening model disadvantages a protected group. The matching plan: obtain or construct a realistic labeled dataset with documented provenance; report selection/error rates disaggregated by group and intersection with confidence intervals; validate the group proxy and state its error; compare against the vendor's fairness claim; audit a sample of individual cases qualitatively for face validity; document the consent/ethics basis for using the data; and state plainly which populations the audit cannot speak to — every number traceable to a logged analysis in the supplementary material.

Statistical and methodological floor

• Uncertainty (CIs or equivalent) on every disaggregated comparison; multiple-comparison awareness across subgroups.
• For qualitative work: the coding scheme, agreement or a defense of a single-coder design, and the interview/observation protocol.
• The compute, data scale, and — for any human-subjects component — the sample and recruitment, not vague feasibility language.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: population / metric-or-method / uncertainty>
[Disaggregation] <groups reported? proxy validity stated? intersections where possible?>
[Ethics basis] <consent / IRB / compensation / re-harm avoidance documented? yes/no>
[Qual rigor] <coding / agreement / reflexivity present where relevant? yes/no>
[Decision-critical next run] <one study extension or analysis>