The Effect of Human v/s Synthetic Test Data and Round-Tripping on Assessment of Sentiment Analysis Systems for Bias-Reference-Cited by-同舟云学术

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The Effect of Human v/s Synthetic Test Data and Round-Tripping on Assessment of Sentiment Analysis Systems for Bias

Published:2023-11-01 Issue: Volume:117 Page:380-389
ISSN:
Container-title:2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
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Author:

Lakkaraju Kausik¹,Gupta Aniket²,Srivastava Biplav¹,Valtorta Marco³,Wu Dezhi⁴

Affiliation:

1. AI Institute, University of South Carolina,Columbia,SC,USA

2. Netaji Subhas University of Technology,Dept. of Computer Science,Delhi,India

3. University of South Carolina,Dept. of Computer Science and Engineering,Columbia,SC,USA

4. University of South Carolina,Dept. of Integrated Information Technology,Columbia,SC,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10431557/10431558/10431645.pdf?arnumber=10431645

Reference21 articles.

1. Advances in automatically rating the trustworthiness of text processing services

2. Examining gender and race bias in two hundred sentiment analysis systems;Kiritchenko,2018

3. Racial disparities in automated speech recognition;Koenecke;Proceedings of the National Academy of Sciences,2020

4. On instabilities of deep learning in image reconstruction and the potential costs of ai;Antun;Proceedings of the National Academy of Sciences,2020

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