Jury Learning: Integrating Dissenting Voices into Machine Learning Models-Reference-Cited by-同舟云学术

Jury Learning: Integrating Dissenting Voices into Machine Learning Models

Published:2022-04-27 Issue: Volume: Page:
ISSN:
Container-title:CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Gordon Mitchell L.¹,Lam Michelle S.²,Park Joon Sung¹,Patel Kayur³,Hancock Jeff⁴,Hashimoto Tatsunori¹,Bernstein Michael S.⁵

Affiliation:

1. Computer Science Department, Stanford University, United States

2. Stanford University, United States

3. Apple Inc, United States

4. Department of Communication, Stanford University, United States

5. Computer Science, Stanford University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3491102.3502004

Reference91 articles.

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4. Sohail Akhtar Valerio Basile and Viviana Patti. 2021. Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection. arxiv:2106.15896 [cs.CL] Sohail Akhtar Valerio Basile and Viviana Patti. 2021. Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection. arxiv:2106.15896 [cs.CL]

5. Jennifer N L Allen Antonio A Arechar Gordon Pennycook and David G Rand. 2020. Scaling Up Fact-Checking Using the Wisdom of Crowds. https://doi.org/10.31234/osf.io/9qdza Jennifer N L Allen Antonio A Arechar Gordon Pennycook and David G Rand. 2020. Scaling Up Fact-Checking Using the Wisdom of Crowds. https://doi.org/10.31234/osf.io/9qdza

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