Research map / Learning theory and algorithms / Prediction and decision losses
HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| tell participants an impartial aggregation algorithm is used, via consequence explanation | peer ranking with conflict of interest · Study 1 review ranking | rank improvement over control (beta): 1.2333 | Helped |
| use policing framing instead of no explanation | peer ranking with conflict of interest · Study 1 review ranking | rank improvement over control (beta): 0.1083 | No clear effect |
| use responsibility externalization framing instead of no explanation | peer ranking with conflict of interest · Study 1 review ranking | rank improvement over control (beta): 0.7333 | No clear effect |
| impartial aggregation (NAIVE-BIPARTITE) instead of Kemeny rule | HirePeer ranking · Study 2 AMT advice ranking | accuracy: 0.9665 -> 0.8884 | Hurt |
| use COMMITTEE impartial aggregation instead of Kemeny rule | HirePeer ranking · Study 2 AMT advice ranking | accuracy: 0.9665 -> 0.8044 | Hurt |
| use k-PARTITE impartial aggregation instead of Kemeny rule | HirePeer ranking · Study 2 AMT advice ranking | accuracy: 0.9665 -> 0.7831 | Hurt |
Closest papers by meaning
- Strategyproof Peer Selection: Mechanisms, Analyses, and Experiments (AAAI 2016 · similarity 0.85)
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- A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding (NeurIPS 2025 · similarity 0.81)
- FairJob: A Real-World Dataset for Fairness in Online Systems (NeurIPS 2024 · similarity 0.81)
- Addressing Bias in Online Selection with Limited Budget of Comparisons (NeurIPS 2024 · similarity 0.80)
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