AI

Pay by the Bit: An Information-Theoretic Metric for Collective Human Judgment

Abstract

We consider the problem of evaluating the performance of human contributors for tasks involving answering a series of questions, each of which has a single correct answer. The answers may not be known a priori.

We assert that the measure of a contributor's judgments is the amount by which having these judgments decreases the entropy of our discovering the answer. This quantity is the pointwise mutual information between the judgments and the answer.

The expected value of this metric is the mutual information between the contributor and the answer prior, which can be computed using only the prior and the conditional probabilities of the contributor's judgments given a correct answer, without knowing the answers themselves.

We also propose using multivariable information measures, such as conditional mutual information, to measure the interactions between contributors' judgments.

These metrics have a variety of applications. They can be used as a basis for contributor performance evaluation and incentives. They can be used to measure the efficiency of the judgment collection process. If the collection process allows assignment of contributors to questions, they can also be used to optimize this scheduling.