Human-aligned AI Model Cards with Weighted Hierarchy Architecture

• URL: http://arxiv.org/abs/2510.06989v2
• Date: Sat, 11 Oct 2025 11:08:49 GMT
• Title: Human-aligned AI Model Cards with Weighted Hierarchy Architecture
• Abstract summary: The proliferation of Large Language Models (LLMs) has led to a burgeoning ecosystem of specialized, domain-specific models.<n>Existing documentation frameworks, such as Model Cards and FactSheets, attempt to standardize reporting but are often static, predominantly qualitative.<n>We introduce the Comprehensive Responsible AI Model Card Framework (CRAI-MCF), a novel approach that transitions from static disclosures to actionable, human-aligned documentation.
• Score: 5.774549987076668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of Large Language Models (LLMs) has led to a burgeoning ecosystem of specialized, domain-specific models. While this rapid growth accelerates innovation, it has simultaneously created significant challenges in model discovery and adoption. Users struggle to navigate this landscape due to inconsistent, incomplete, and imbalanced documentation across platforms. Existing documentation frameworks, such as Model Cards and FactSheets, attempt to standardize reporting but are often static, predominantly qualitative, and lack the quantitative mechanisms needed for rigorous cross-model comparison. This gap exacerbates model underutilization and hinders responsible adoption. To address these shortcomings, we introduce the Comprehensive Responsible AI Model Card Framework (CRAI-MCF), a novel approach that transitions from static disclosures to actionable, human-aligned documentation. Grounded in Value Sensitive Design (VSD), CRAI-MCF is built upon an empirical analysis of 240 open-source projects, distilling 217 parameters into an eight-module, value-aligned architecture. Our framework introduces a quantitative sufficiency criterion to operationalize evaluation and enables rigorous cross-model comparison under a unified scheme. By balancing technical, ethical, and operational dimensions, CRAI-MCF empowers practitioners to efficiently assess, select, and adopt LLMs with greater confidence and operational integrity.

Related papers

• Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection [0.0]
  Self-adapting generative AI systems render point-in-time validation inadequate.<n>We develop a rigorous telemetry architecture for such models, operating simultaneously in discrete and continuous time.<n>We formalize this as the Model Hiding Problem and provide a systematic taxonomy of six distinct attack strategies against the Part I architecture.
  arXiv  Detail & Related papers  (2026-08-10T03:20:06Z)
• Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models [62.62160094849002]
  RADIANT is an engineering methodology that combines MDE with Multi-Agent Large Language Models.<n>It automatically generates heterogeneous models across engineering phases.<n>It provides exact, automated change-impact analysis.
  arXiv  Detail & Related papers  (2026-07-18T08:50:55Z)
• Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification [53.763212981479455]
  We propose a novel framework with Dual-side Verification (Opt-Verifier) from both structure and solution perspectives.<n> Experiments on popular benchmarks demonstrate that our approach achieves over 20% improvement in accuracy.
  arXiv  Detail & Related papers  (2026-05-28T08:09:52Z)
• All LCA models are wrong. Are some of them useful? Towards open computational LCA in ICT [4.020492359480635]
  Life Cycle Assessment (LCA) is increasingly used to quantify and regulate the environmental impacts of Information and Communication Technology systems.<n>Since direct biosphere measurements are complicated to perform, we claim that the environmental impact assessment of ICT relies heavily on models.<n>We argue that such systems require an extra-high level of carefulness in construction, calibration, integration, and interpretation.
  arXiv  Detail & Related papers  (2026-04-07T14:36:56Z)
• CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks [54.04030169323115]
  We introduce CREDIT, a certified ownership verification against Model Extraction Attacks (MEAs)<n>We quantify the similarity between DNN models, propose a practical verification threshold, and provide rigorous theoretical guarantees for ownership verification based on this threshold.<n>We extensively evaluate our approach on several mainstream datasets across different domains and tasks, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-23T23:36:25Z)
• Heterogeneous Model Alignment in Digital Twin [0.0]
  Key challenge in model-driven DTs is aligning heterogeneous models across abstraction layers.<n>Existing methods, relying on static mappings and manual updates, are often inflexible, error-prone, and risk compromising data integrity.<n>We present a heterogeneous model alignment approach for multi-layered, model-driven DTs.
  arXiv  Detail & Related papers  (2025-12-17T10:36:55Z)
• Every Step Counts: Decoding Trajectories as Authorship Fingerprints of dLLMs [63.82840470917859]
  We show that the decoding mechanism of dLLMs can be used as a powerful tool for model attribution.<n>We propose a novel information extraction scheme called the Directed Decoding Map (DDM), which captures structural relationships between decoding steps and better reveals model-specific behaviors.
  arXiv  Detail & Related papers  (2025-10-02T06:25:10Z)
• Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling [23.919163488129985]
  The Structural Reward Model (SRM) is a modular framework integrating side-branch and auxiliary feature generators.<n>By introducing fine-grained dimensions, RMs enable interpretable and efficient evaluations, targeted diagnostics and optimization.
  arXiv  Detail & Related papers  (2025-09-29T18:09:25Z)
• OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation [91.45421429922506]
  OneCAT is a unified multimodal model that seamlessly integrates understanding, generation, and editing.<n>Our framework eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference.
  arXiv  Detail & Related papers  (2025-09-03T17:29:50Z)
• Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well [31.013018198280506]
  This paper introduces a novel framework, Conformalized Exceptional Model Mining.<n>It combines the rigor of Conformal Prediction with the explanatory power of Exceptional Model Mining.<n>We develop a new model class, mSMoPE, which quantifies uncertainty through conformal prediction's rigorous coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-21T13:43:14Z)
• AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability [84.52205243353761]
  Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment.<n>We investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation.
  arXiv  Detail & Related papers  (2025-04-06T20:35:44Z)
• SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era [1.4835379864550937]
  We argue for the urgent need to establish a structured reporting standard for surrogate models.<n>By promoting a standardised yet flexible framework, we aim to improve the reliability of surrogate modelling.
  arXiv  Detail & Related papers  (2025-02-10T18:31:15Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.