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Title: A Fairness-Aware Fusion Framework for Multimodal Cyberbullying Detection
Recent reports of bias in multimedia algorithms (e.g., lesser accuracy of face detection for women and persons of color) have underscored the urgent need to devise approaches which work equally well for different demographic groups. Hence, we posit that ensuring fairness in multimodal cyber-bullying detectors (e.g., equal performance irrespective of the gender of the victim) is an important research challenge. We propose a fairness-aware fusion framework that ensures that both fairness and accuracy remain important considerations when combining data coming from multiple modalities. In this Bayesian fusion framework, the inputs coming from different modalities are combined in a way that is cognizant of the different confidence levels associated with each feature and the interdependencies between features. Specifically, this framework assigns weights to different modalities not just based on accuracy but also their fairness. Results of applying the framework on a multimodal (visual + text) cyberbullying detection problem demonstrate the value of the proposed framework in ensuring both accuracy and fairness.  more » « less
Award ID(s):
1915790
PAR ID:
10231393
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)
Page Range / eLocation ID:
166 to 173
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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