# Exploring Fusion Techniques in Multimodal AI-Based Recruitment: Insights from FairCVdb

arXiv: https://arxiv.org/abs/2407.16892 · HTML: https://mlsanity.com/paper/2407.16892/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [Dynamic Multimodal Fusion](https://mlsanity.com/paper/2204.00102/) (2022)
- [Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment](https://mlsanity.com/paper/2302.10908/) (2023)
- [On the Benefits of Early Fusion in Multimodal Representation Learning](https://mlsanity.com/paper/2011.07191/) (2020)
- [Bias in Multimodal AI: Testbed for Fair Automatic Recruitment](https://mlsanity.com/paper/2004.07173/) (2020)
- [Power of Explanations: Towards automatic debiasing in hate speech detection](https://mlsanity.com/paper/2209.09975/) (2022)
- [Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering](https://mlsanity.com/paper/2402.10756/) (2024)

## Built on by (most cited first, 2 of 2)

- [MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs](https://mlsanity.com/paper/2509.08156/) (2025)
- [U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation](https://mlsanity.com/paper/2412.10679/) (2024)

## Abstract (arXiv)

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