More is Less? A Simulation-Based Approach to Dynamic Interactions between Biases in Multimodal Models

• URL: http://arxiv.org/abs/2412.17505v1
• Date: Mon, 23 Dec 2024 12:04:28 GMT
• Title: More is Less? A Simulation-Based Approach to Dynamic Interactions between Biases in Multimodal Models
• Authors: Mounia Drissi,
• Abstract summary: This study proposes a systemic framework for analyzing dynamic multimodal bias interactions. Using the MMBias dataset, this study adopts a simulation-based approach to compute bias scores for text-only, image-only, and multimodal embeddings. A framework is developed to classify bias interactions as amplification, mitigation, and neutrality.
• Score: 0.0
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• Abstract: Multimodal machine learning models, such as those that combine text and image modalities, are increasingly used in critical domains including public safety, security, and healthcare. However, these systems inherit biases from their single modalities. This study proposes a systemic framework for analyzing dynamic multimodal bias interactions. Using the MMBias dataset, which encompasses categories prone to bias such as religion, nationality, and sexual orientation, this study adopts a simulation-based heuristic approach to compute bias scores for text-only, image-only, and multimodal embeddings. A framework is developed to classify bias interactions as amplification (multimodal bias exceeds both unimodal biases), mitigation (multimodal bias is lower than both), and neutrality (multimodal bias lies between unimodal biases), with proportional analyzes conducted to identify the dominant mode and dynamics in these interactions. The findings highlight that amplification (22\%) occurs when text and image biases are comparable, while mitigation (11\%) arises under the dominance of text bias, highlighting the stabilizing role of image bias. Neutral interactions (67\%) are related to a higher text bias without divergence. Conditional probabilities highlight the text's dominance in mitigation and mixed contributions in neutral and amplification cases, underscoring complex modality interplay. In doing so, the study encourages the use of this heuristic, systemic, and interpretable framework to analyze multimodal bias interactions, providing insight into how intermodal biases dynamically interact, with practical applications for multimodal modeling and transferability to context-based datasets, all essential for developing fair and equitable AI models.

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