  Despite the large body of work on fairness-aware learning for individual
modalities like tabular data, images, and text, less work has been done on
multimodal data, which fuses various modalities for a comprehensive analysis.
In this work, we investigate the fairness and bias implications of multimodal
fusion techniques in the context of multimodal AI-based recruitment systems
using the FairCVdb dataset. Our results show that early-fusion closely matches
the ground truth for both demographics, achieving the lowest MAEs by
integrating each modality's unique characteristics. In contrast, late-fusion
leads to highly generalized mean scores and higher MAEs. Our findings emphasise
the significant potential of early-fusion for accurate and fair applications,
even in the presence of demographic biases, compared to late-fusion. Future
research could explore alternative fusion strategies and incorporate
modality-related fairness constraints to improve fairness. For code and
additional insights, visit:
https://github.com/Swati17293/Multimodal-AI-Based-Recruitment-FairCVdb
