Mitigating Cognitive Biases in Multi-Criteria Crowd Assessment

TL;DR

This paper investigates cognitive biases in multi-criteria crowdsourcing assessments and proposes Bayesian models to reduce biases, leading to more accurate quality evaluations.

Contribution

It introduces two Bayesian opinion aggregation models that explicitly consider inter-criteria relations to mitigate cognitive biases in crowdsourced multi-criteria assessments.

Findings

Proposed models effectively reduce cognitive biases.

Incorporating inter-criteria relations improves aggregation accuracy.

Experimental results validate the effectiveness of the models.

Abstract

Crowdsourcing is an easy, cheap, and fast way to perform large scale quality assessment; however, human judgments are often influenced by cognitive biases, which lowers their credibility. In this study, we focus on cognitive biases associated with a multi-criteria assessment in crowdsourcing; crowdworkers who rate targets with multiple different criteria simultaneously may provide biased responses due to prominence of some criteria or global impressions of the evaluation targets. To identify and mitigate such biases, we first create evaluation datasets using crowdsourcing and investigate the effect of inter-criteria cognitive biases on crowdworker responses. Then, we propose two specific model structures for Bayesian opinion aggregation models that consider inter-criteria relations. Our experiments show that incorporating our proposed structures into the aggregation model is effective to reduce the cognitive biases and help obtain more accurate aggregation results.