Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms
<|reference_start|>Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms: Because high-quality data is like oxygen for AI systems, effectively eliciting information from crowdsourcing workers has become a first-order problem for developing high-performance machine learning algorithms. Two prevalent paradigms, spot-checking and peer prediction, enable the design of mechanisms to evaluate and incentivize high-quality data from human labelers. So far, at least three metrics have been proposed to compare the performances of these techniques [33, 8, 3]. However, different metrics lead to divergent and even contradictory results in various contexts. In this paper, we harmonize these divergent stories, showing that two of these metrics are actually the same within certain contexts and explain the divergence of the third. Moreover, we unify these different contexts by introducing \textit{Spot Check Equivalence}, which offers an interpretable metric for the effectiveness of a peer prediction mechanism. Finally, we present two approaches to compute spot check equivalence in various contexts, where simulation results verify the effectiveness of our proposed metric.<|reference_end|>
@article{xu2024spot,
    title={Spot Check Equivalence: an Interpretable Metric for Information
  Elicitation Mechanisms},
    author={Shengwei Xu, Yichi Zhang, Paul Resnick, Grant Schoenebeck},
    journal={arXiv preprint arXiv:2402.13567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13567},
    primaryClass={cs.LG cs.AI cs.GT}
}