Interpretable Attention Mechanisms for Trustworthy Large Vision-Language Models

Abstract

Large vision-language models have become infrastructural components of multimodal search, assistive technologies, autonomous inspection, medical triage, and public-sector decision support. Their capacity to align visual evidence with linguistic reasoning depends heavily on attention mechanisms that route information across modalities, yet those mechanisms remain difficult to interpret, audit, and govern. This paper examines interpretable attention as a system-level property rather than a purely architectural feature. It argues that trustworthy large vision-language models require attention interfaces that are causally faithful, operationally stable, and institutionally accountable. The analysis considers how attention transparency intersects with robustness, fairness, sustainability, deployment, and policy. It distinguishes between explanatory plausibility and causal faithfulness, showing that visually appealing attention maps can mislead when they are disconnected from model decisions. The paper then evaluates architectural trade-offs, including cross-modal routing, token pruning, sparse attention, and head specialization, and explains how each affects interpretability, latency, energy use, and failure modes. Governance implications are considered through model cards, datasheets, internal audits, and human oversight. The discussion emphasizes that interpretability must be designed into the lifecycle of data collection, training, evaluation, deployment, and incident response. The conclusion recommends layered attention transparency, standard audit artifacts, and socio-technical evaluation that treats interpretability as a condition for institutional trust rather than a cosmetic explanation layer.