A systematic review of 55 papers finds explainability for multimodal attention-based models is dominated by attention-weight visualizations, while evaluation remains mostly qualitative and non-standardized.
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The paper introduces the AI-Identity Risk Taxonomy (AIRT) with 37 risk categories and the Machine Identity Governance Taxonomy (MIGT) as a six-domain framework to close gaps in technical, regulatory, and cross-jurisdictional governance of AI machine identities.
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Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
A systematic review of 55 papers finds explainability for multimodal attention-based models is dominated by attention-weight visualizations, while evaluation remains mostly qualitative and non-standardized.
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Who Governs the Machine? A Machine Identity Governance Taxonomy (MIGT) for AI Systems Operating Across Enterprise and Geopolitical Boundaries
The paper introduces the AI-Identity Risk Taxonomy (AIRT) with 37 risk categories and the Machine Identity Governance Taxonomy (MIGT) as a six-domain framework to close gaps in technical, regulatory, and cross-jurisdictional governance of AI machine identities.