Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms

Understand

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.