Physics-based phenomenological characterization of cross-modal bias in multimodal models
arXiv:2602.20624v1 Announce Type: new Abstract: The term 'algorithmic fairness' is used to evaluate whether AI models operate fairly in both comparative (where fairness is understood as formal equality, such as "treat like cases as like") and non-comparative (where unfairness arises from the model's inaccuracy, arbitrariness, or inscrutability) contexts. Recent advances in multimodal large language models (MLLMs) are breaking new ground in multimodal understanding, reasoning, and generation; however, we argue that inconspicuous distortions arising from complex multimodal interaction dynamics can lead to systematic bias. The purpose of this position paper is twofold: first, it is intended to acquaint AI researchers with phenomenological explainable approaches that rely on the physical entities that the machine experiences during training/inference, as opposed to the traditional cognitivist symbolic account or metaphysical approaches; second, it is to state that this phenomenological do
arXiv:2602.20624v1 Announce Type: new Abstract: The term 'algorithmic fairness' is used to evaluate whether AI models operate fairly in both comparative (where fairness is understood as formal equality, such as "treat like cases as like") and non-comparative (where unfairness arises from the model's inaccuracy, arbitrariness, or inscrutability) contexts. Recent advances in multimodal large language models (MLLMs) are breaking new ground in multimodal understanding, reasoning, and generation; however, we argue that inconspicuous distortions arising from complex multimodal interaction dynamics can lead to systematic bias. The purpose of this position paper is twofold: first, it is intended to acquaint AI researchers with phenomenological explainable approaches that rely on the physical entities that the machine experiences during training/inference, as opposed to the traditional cognitivist symbolic account or metaphysical approaches; second, it is to state that this phenomenological doctrine will be practically useful for tackling algorithmic fairness issues in MLLMs. We develop a surrogate physics-based model that describes transformer dynamics (i.e., semantic network structure and self-/cross-attention) to analyze the dynamics of cross-modal bias in MLLM, which are not fully captured by conventional embedding- or representation-level analyses. We support this position through multi-input diagnostic experiments: 1) perturbation-based analyses of emotion classification using Qwen2.5-Omni and Gemma 3n, and 2) dynamical analysis of Lorenz chaotic time-series prediction through the physical surrogate. Across two architecturally distinct MLLMs, we show that multimodal inputs can reinforce modality dominance rather than mitigate it, as revealed by structured error-attractor patterns under systematic label perturbation, complemented by dynamical analysis.
Executive Summary
This article explores the concept of cross-modal bias in multimodal large language models (MLLMs) and proposes a physics-based phenomenological approach to characterize and address this issue. The authors argue that traditional methods of analyzing bias in AI models are insufficient and that a more nuanced understanding of the complex interactions between different modalities is required. Through multi-input diagnostic experiments, the authors demonstrate that multimodal inputs can actually reinforce modality dominance rather than mitigate it, leading to systematic bias. This research has significant implications for the development of fair and transparent AI systems, particularly in the context of multimodal models.
Key Points
- ▸ The article highlights the limitations of traditional methods for analyzing bias in AI models, particularly in the context of multimodal interactions.
- ▸ The authors propose a physics-based phenomenological approach to characterize and address cross-modal bias in MLLMs.
- ▸ The research demonstrates that multimodal inputs can reinforce modality dominance, leading to systematic bias, and suggests that this is a key area of concern for the development of fair AI systems.
Merits
Strength in methodology
The authors employ a novel and innovative approach to analyzing cross-modal bias, drawing on insights from physics and phenomenology to develop a more nuanced understanding of multimodal interactions.
Contribution to the field
The research has significant implications for the development of fair and transparent AI systems, particularly in the context of multimodal models, and highlights the need for more sophisticated methods of analyzing bias in AI models.
Methodological rigor
The authors' use of multi-input diagnostic experiments and dynamical analysis provides a high level of methodological rigor and suggests that the findings are robust and reliable.
Demerits
Limited scope
The research is focused primarily on MLLMs and may not generalize to other types of AI models or applications.
Need for further validation
While the research demonstrates a clear and significant effect of multimodal inputs on modality dominance, further validation is required to confirm the generalizability of these findings to other contexts.
Complexity of physics-based approach
The authors' use of a physics-based approach may be challenging to implement and interpret, particularly for researchers without a strong background in physics or phenomenology.
Expert Commentary
This research is a significant contribution to the field of AI and has the potential to shape the development of multimodal models in the future. The authors' use of a physics-based phenomenological approach is innovative and rigorous, and the findings are well-supported by the data. However, as with any research, there are limitations and challenges that must be addressed. The complexity of the physics-based approach may be a barrier to implementation and interpretation, and further validation is required to confirm the generalizability of the findings. Nevertheless, this research provides a valuable contribution to the ongoing conversation about the development of fair and transparent AI systems.
Recommendations
- ✓ Developers should carefully consider the potential for cross-modal bias in MLLMs and develop more sophisticated methods for analyzing and mitigating this bias.
- ✓ Policymakers should prioritize the development of fair and transparent AI systems, particularly in the context of MLLMs, and consider implementing regulations or guidelines to ensure the responsible development and deployment of these models.
