AuditMM: A Framework for Auditing Bias and Interpretability in Multimodal AI Systems

AuditMM: A Framework for Auditing Bias and Interpretability in Multimodal AI Systems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 January 2026 V1 Latest version Share on AuditMM: A Framework for Auditing Bias and Interpretability in Multimodal AI Systems Authors : Jay Oza 0009-0002-0102-7478 [email protected] and Hrishikesh Yadav 0009-0009-9584-6714 Authors Info & Affiliations https://doi.org/10.22541/au.176851572.28719843/v1 134 views 75 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Multimodal AI systems (combining text, image, audio, etc.) are increasingly used in high-stakes domains, but are under-audited for fairness, transparency, and bias amplification across modalities. We propose AuditMM, a high-level framework for systematically auditing multimodal systems: covering data, preprocessing, feature extraction & encoding, fusion, decision output, and post-deployment monitoring. AuditMM includes synthetic and counterfactual benchmarks, modality-specific and fused-model metrics, and diagnostic tools to locate sources of bias. We present a small experiment applying AuditMM on a benchmark text+image classification task, showing how fused models can amplify disparity relative to single-modality baselines, and demonstrate probing and attention-weight diagnostics. Our results indicate actionable insights for mitigation of bias in the multimodal training and auditing pipeline. Supplementary Material File (auditmm__a_framework_for_auditing_bias_and_interpretability_in_multimodal_ai_systems (1).pdf) Download 525.11 KB Information & Authors Information Version history V1 Version 1 15 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence audit bias interpretability large language models multimodal Authors Affiliations Jay Oza 0009-0002-0102-7478 [email protected] View all articles by this author Hrishikesh Yadav 0009-0009-9584-6714 View all articles by this author Metrics & Citations Metrics Article Usage 134 views 75 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jay Oza, Hrishikesh Yadav. AuditMM: A Framework for Auditing Bias and Interpretability in Multimodal AI Systems. Authorea . 15 January 2026. DOI: https://doi.org/10.22541/au.176851572.28719843/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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