| { |
| "schema_version": 1, |
| "title": "Repro: Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints", |
| "emoji": "chart", |
| "space_id": "snaykey/repro-fair-crowd-aggregation", |
| "paper": { |
| "openreview_id": "niQUth28zn" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-niQUth28zn" |
| ], |
| "updated_at": "2026-07-27T00:00:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Repro: Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1", |
| "title": "The demographic parity gap of majority-vote aggregated labels is bounded above by an exponentially decaying term in the number of annotators R, of the form sum over groups a of E[e^{-R K_phi(a,X)}] (Proposition 3.2).", |
| "file": "pages/claim-1/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2", |
| "title": "Under interpretable conditions on annotator skill, both Majority Vote and Bayesian aggregation are shown to achieve asymptotically unbiased fairness gaps relative to ground-truth labels as crowd size grows (Theorem 3.4).", |
| "file": "pages/claim-2/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3", |
| "title": "For majority voting with R annotators, the aggregated fairness gap is bounded by epsilon(R) times the sum of individual annotators' fairness gaps, showing majority voting can amplify rather than average out bias (Proposition 3.6).", |
| "file": "pages/claim-3/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4", |
| "title": "Convergence of the fairness gap to the ground-truth value requires a majority of annotators to have skill exceeding 0.5 plus a margin epsilon; without this, majority voting fails to converge (Section 3, following Proposition 3.6).", |
| "file": "pages/claim-4/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5", |
| "title": "FairCrowd, a post-processing method, enforces a strict epsilon-fairness constraint on the output of any aggregation rule while provably bounding the resulting accuracy loss (Theorem 4.1).", |
| "file": "pages/claim-5/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| } |
| } |
|
|
