Mitigating Cognitive Biases in Multi-Criteria Crowd Assessment
This work addresses the issue of unreliable human judgments in crowdsourcing for multi-criteria tasks, offering a method to improve accuracy, though it is incremental as it builds on existing aggregation models.
The study tackled the problem of cognitive biases in multi-criteria crowd assessments, which lower credibility, by proposing Bayesian opinion aggregation models that incorporate inter-criteria relations, resulting in reduced biases and more accurate aggregation results.
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.