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 an d 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
The increasing popularity of decision-making algorithms has raised concerns about bias in
decision-making, especially towards specific social groups defined by protected attributes
such as gender and ethnicity [
Early-fusion typically concatenates features from diferent modalities early on, creating a unified representation of the data [ 10], which simplifies training and efectively captures interactions between modalities [11]. Late-fusion, on the other hand, processes each modality individually before combining their outputs at a later stage, ofering flexibility by allowing diferent processing pathways for individual modalities [12]. While late-fusion captures modality-specific patterns more accurately, it may overlook lower-level interactions between modalities [13]. By investigating these two fusion strategies, we aim to gain insight into how they impact bias and fairness in automated recruitment processes.
Dataset: The FairCVdb dataset [
Evaluation Metrics: Following [14], we use Mean Absolute Error (MAE) to measure prediction error and Kullback-Leibler divergence (KL) to assess demographic bias. For gender, we compare score distributions for males and females; for ethnicity, we perform pairwise comparisons and report the average divergence.
Models: We extend the testbed [14] to facilitate multimodal recruitment learning by including early-fusion and late-fusion techniques for all three modalities (textual, visual, tabular). Simulated setups: We investigated both i) unbiased ideal world setup and ii) real-world setups gender- and ethnicity- biased).
In unbiased ideal-world (Neutral): We note that the ground-truth distributions are closely aligned for both demographics (c.f., Figure 1a). W.r.t. individual modalities (c.f., Figure 1b - 1d), we can see that tabular modality exhibits a lower score distribution centred around a mean of h t u r T d n u o r G ) a ( r a l u b a T ) b ( l a u t x e T ) c ( l a u s i V ) d ( n o i s u F y l r a E ) e ( n o i s u F e t a L ) f (
0.4 with a negatively-skewed distribution, indicating that it tends to underestimate the groundtruth. The presence of a bimodal distribution in the textual modality is specially intriguing, demonstrating its ability to diferentiate between instances with high and low scores. The Visual modality, on the other hand, exhibits extreme behaviour by concentrating the distribution of nearly the entire population within a very narrow range [0.39–0.44] (c.f., Figure 1d), pointing an over-generalization of the mean score to all instances. Interestingly, late-fusion produces the least biased results for both demographics. However, while aggregating the decisions from diferent modalities, its average decision gets afected by the extremity of the visual modality, leading to over-generalization of the mean score, consequently resulting in higher MAEs (c.f., Figure 1f). In contrast, early-fusion delivers the most accurate predictions with the lowest MAEs (c.f., Figure 1e) by efectively learning and resolving the unique peculiarities of each modality, such as underestimation, over-generalization, and bimodal distribution, resulting in a shape that resembles the ground-truth (c.f., Figure 1a, 1e).
In biased real-world setups (Gender/Ethnicity-Biased): We observe that the ground-truth distributions are not aligned for both demographics (c.f., Figure 1a). W.r.t. individual modalities (c.f., Figure 1b - 1d), we see that the tabular modality continues to exhibit underestimation across all demographics, which leads to close alignment of the demographic specific distributions (c.f., Figure 1b(2) and b(4)). With textual modality we notice a misalignment of distribution w.r.t. gender demographics with a favourable skewness for males. However, no such bias is observed w.r.t. ethnicity, indicating a possibility of gender-skewness being much higher than the ethnicity-skewness for the job-related words in the embedded space. Conversely, the visual modality demonstrates the most extreme bias for both demographics. Regarding gender, it shows a positive bias towards males, while for ethnicity, it overgeneralizes Asians, discriminates against Blacks, and favours Caucasians. Continuing the trend established in the neutral setup, Early fusion consistently mimics the ground-truth for both demographics, yielding the lowest MAEs while maintaining fairness. Late-fusion, while also following its trend, tends to over-generalize the mean score, resulting in higher MAEs but also higher KL scores.
In general, leveraging multimodal data can enhance performance and mitigate bias compared to relying on a single modality. However, blindly fusing all modalities may not always yield the best results. For instance, the tabular in gender-biased setup (c.f., Figure 1b(2)) and the textual in ethnicity-biased setup (c.f., Figure 1c(4)) outperformed both fusion strategies. We hypothesise that late-fusion exacerbates biases by independently learning biased models for each modality, cumulatively impacting decision fairness, while early-fusion ofers greater flexibility and generally yields fairer outcomes with lower prediction error. Dataset diversity and biases may have influenced these findings, highlighting the need to assess robustness across multiple datasets, domains, and fusion strategies. We contemplate that in the future, exploring mid-fusion strategies could enhance fairness and accuracy in decision-making through strategic selection and a combination of modalities.
In our study, we used the FairCVdb dataset to investigate the bias implications of early- and late- fusion strategies in multimodal AI-based recruitment. We assessed biases in gender and ethnicity demographics across both unbiased (neutral) and real-world (gender/ethnicity-biased) setups. Our findings reveal that early-fusion closely mimics the ground truth for both demographics, achieving the lowest MAEs by efectively incorporating the unique characteristics of each modality. In contrast, late-fusion leads to highly over-generalized mean scores, resulting in higher MAEs. Our evaluation underscores the significant potential of early-fusion for applications requiring both accuracy and fairness, providing robust solutions even in the presence of demographic biases. Based on the results, we speculate that mid-fusion strategies may enhance fairness and accuracy by strategically selecting and combining modalities. Exploring these findings across diverse datasets and domains beyond hiring could further broaden the study’s impact and relevance. Ethics statement: Understanding the risks of using simulated or synthetic data is crucial for fairness, transparency, and efectiveness in automated hiring processes.
This research work is funded by the European Union under the Horizon Europe MAMMOth project, Grant Agreement ID: 101070285. UK participant in Horizon Europe Project MAMMOth is supported by UKRI grant number 10041914 (Trilateral Research LTD). The research is also supported by the EU Horizon Europe project STELAR, Grant Agreement ID: 101070122. tional neural networks, in: 2020 IEEE 23rd international conference on information fusion (FUSION), IEEE, 2020, pp. 1–6. [10] L. M. Pereira, A. Salazar, L. Vergara, A comparative analysis of early and late fusion for the multimodal two-class problem, IEEE Access (2023). [11] G. Barnum, S. Talukder, Y. Yue, On the benefits of early fusion in multimodal representation learning, arXiv preprint arXiv:2011.07191 (2020). [12] L. M. Pereira, A. Salazar, L. Vergara, On comparing early and late fusion methods, in: International Work-Conference on Artificial Neural Networks (IWANN), Springer, 2023, pp. 365–378. [13] K. Bayoudh, R. Knani, F. Hamdaoui, A. Mtibaa, A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets, The Visual Computer 38 (2022) 2939–2970. [14] A. Peña, I. Serna, A. Morales, J. Fierrez, A. Ortega, A. Herrarte, M. Alcantara, J. OrtegaGarcia, Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment, SN Computer Science 4 (2023) 434.