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Title: Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying, fake news and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now publicly available for sentiment detection. Users can easily incorporate these APIs in their applications. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasing fairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two-actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications.  more » « less
Award ID(s):
1915790
PAR ID:
10231394
Author(s) / Creator(s):
;
Date Published:
Journal Name:
In Proceedings of the 2nd International Workshop on Fairness, Accountability, Transparency and Ethics in Multimedia
Page Range / eLocation ID:
13 to 19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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