Optimal Fair Aggregation of Crowdsourced Noisy Labels

Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints

Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort. Aggregating subjective labels, though, may amplify individual biases, particularly regarding sensitive features, raising fairness concerns. Nonetheless, fairness in crowdsourced aggregation remains largely unexplored, with no existing convergence guarantees and only limited post-processing approaches for...