Exploring and repairing gender fairness violations in word embedding-based sentiment analysis model through adversarial patches

Exploring and repairing gender fairness violations in word embedding-based sentiment analysis model through adversarial patches

With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the d...

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Bibliographic Details
Main Authors: KHOO, Lin Sze, BAY, Jia Qi, YAP, Ming Lee Kimberly, LIM, Mei Kuan, CHONG, Chun Yong, YANG, Zhou, LO, David
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
Fairness testing
Automated program repair
Sentiment analysis
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/8514
https://ink.library.smu.edu.sg/context/sis_research/article/9517/viewcontent/Exploring_and_Repairing_Gender_Fairness_Violations_in_Word_Embedding_based_Sentiment_Analysis_Model_through_Adversarial_Patches.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/8514
https://ink.library.smu.edu.sg/context/sis_research/article/9517/viewcontent/Exploring_and_Repairing_Gender_Fairness_Violations_in_Word_Embedding_based_Sentiment_Analysis_Model_through_Adversarial_Patches.pdf

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