Frustratingly Easy Truth Discovery

Reshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben-Shabat, Gal Cohensius, Lirong Xia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform. In this paper, we consider an extremely simple heuristic for estimating workers' competence using average proximity to other workers. We prove that this estimates well the actual competence level and enables separating high and low quality workers in a wide spectrum of domains and statistical models. Under Gaussian noise, this simple estimate is the unique solution to the Maximum Likelihood Estimator with a constant regularization factor. Finally, weighing workers according to their average proximity in a crowdsourcing setting, results in substantial improvement over unweighted aggregation and other truth discovery algorithms in practice.

Original languageEnglish
Title of host publicationAAAI-23 Technical Tracks 5
EditorsBrian Williams, Yiling Chen, Jennifer Neville
Pages6074-6083
Number of pages10
ISBN (Electronic)9781577358800
StatePublished - 27 Jun 2023
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 7 Feb 202314 Feb 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume37

Conference

Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUnited States
CityWashington
Period7/02/2314/02/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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