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Does Exposure to Diverse Perspectives Mitigate Biases in Crowd work? An Explorative Study

  • Purdue University
  • Washington University St. Louis

研究成果: 圖書/報告稿件的類型會議稿件同行評審

14 引文 斯高帕斯(Scopus)

摘要

Earlier research has shown the promise of enabling worker interactions in crowd work to mitigate worker biases and improve the quality of crowd work. In this study, we focus on one characteristic of the interacting workers that may influence the effectiveness of worker interactions in enhancing crowd work—the diversity of perspectives that the interacting workers bring together—and we explore whether and how interactions between a set of workers holding different perspectives can help mitigate biases in crowd work. Through two sets of randomized experiments, we find that whether interactions between workers with different perspectives can help mitigate biases in crowd work depends on task properties. We also find no conclusive evidence in our experimental settings suggesting that interactions among workers with diverse perspectives reduce biases in crowd work to a larger extent compared to interactions among workers with similar perspectives.

原文英語
主出版物標題HCOMP 2020 - Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing
編輯 Lora Aroyo, Elena Simperl
發行者Association for the Advancement of Artificial Intelligence
頁面155-158
頁數4
ISBN(列印)9781577358480
DOIs
出版狀態已出版 - 2020
對外發佈
事件8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020 - Virtual, Online
持續時間: 25 10 202029 10 2020

出版系列

名字Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
8
ISSN(列印)2769-1330
ISSN(電子)2769-1349

Conference

Conference8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020
城市Virtual, Online
期間25/10/2029/10/20

文獻附註

Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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