AI-Generated Factor Cards

• AI-generated factor cards are defined as AI-augmented artifacts that capture, prioritize, and structure factors for comprehensive AI model evaluation.
• They utilize a modular, hierarchical organization with quantitative reference priors and sufficiency thresholds to guide transparent documentation.
• Applications include risk assessment, lifecycle transparency, and financial factor analysis, ensuring standardized and actionable comparisons.

AI-generated factor cards are structured artifacts—produced or augmented by artificial intelligence methods—designed to systematically capture, document, and operationalize sets of factors relevant for evaluating, comparing, or deploying AI models, systems, or data-driven processes. These cards formalize not just model parameters or metadata, but quantitatively prioritized and hierarchically organized dimensions spanning technical, operational, and ethical considerations. Recent research demonstrates their applicability to model documentation (e.g., LLMs), risk assessment, system lifecycle transparency, and quantitative investment strategies, with a focus on cross-model comparability and actionable sufficiency.

1. Defining AI-Generated Factor Cards

AI-generated factor cards refer to documentation artifacts constructed with significant AI assistance—either by extracting, ranking, and organizing salient factors from empirical corpora or by leveraging machine learning models to algorithmically generate, validate, or summarize the factors themselves. A "factor" in this context is the smallest self-contained unit of information relevant for the evaluation, comparison, or responsible adoption of a model or system (e.g., “data provenance”, “robustness metric”, “intended use”, “environmental impact”).

The Comprehensive Responsible AI Model Card Framework (CRAI-MCF) (Yang et al., 8 Oct 2025) exemplifies such cards, where 217 atomic factors relevant to LLM documentation were distilled from a corpus of 240 projects by empirical analysis, then structured into an explicit weighted hierarchy. These factors are rendered actionable through quantitative mechanisms, departing from static, narrative-driven reporting schemes.

2. Hierarchical and Modular Organization

A distinguishing feature of modern AI-generated factor cards is the hierarchical modular architecture. Rather than a flat checklist, factors are grouped into mutually exclusive and collectively exhaustive top-level modules optimized for navigability and auditability. CRAI-MCF, for instance, organizes its 217 normalized parameters into the following eight Level-0 modules, each aligned to responsible AI values:

Module	Value Domains	Example Parameters
Model Details	Usability, Transparency	Name, objectives, license
Model Use	Transparency	Intended use, misuse, scope
Data	Transparency, Accountability	Data source, provenance, consent
Training	Sustainability	Hyperparameters, compute resources
Performance & Limitations	Accountability	Metrics, fairness, robustness
Feedback	Interactivity, Accountability	Incident reporting, feedback loops
Broader Implications	Sustainability, Ethics	Societal risks, ethical concerns
More Info	Usability, Transparency	Configs, seeds, scripts

Each module may contain up to five levels of sub-factors, enabling both rapid survey and deep drill-down. The architecture is strictly containment-based to optimize cognitive ergonomics.

3. Quantitative Sufficiency: Reference Priors and Baselines

A core methodological advance is the transition from qualitative, template-based reporting to a frequency-weighted, quantifiable notion of documentation sufficiency. This is operationalized through two mechanisms (Yang et al., 8 Oct 2025):

1. Parameter-level Reference Priors:
   • Each parameter $p_i$ is assigned a prior $s_i = \frac{f_i}{N}$, where $f_i$ is the empirical frequency of that parameter's presence in a reference corpus of $N$ projects.
   • $s_i$ constitutes a salience signal—prioritizing factors most salient in high-adoption practice, not prescribing normative weights.
2. Module-level Sufficiency Thresholds:
   • For module $M$ with cumulative attainable prior $S_M = \sum_{i \in M} s_i$:

   $$\mathrm{BaselineScore}(M) = \left( \frac{O_M}{O_{\text{All}}} + \frac{A_M}{A_{\text{All}}} \right) \cdot \frac{S_M}{2}$$

• Here, $O_M/O_{\text{All}}$ is the observed empirical coverage, and $A_M/A_{\text{All}}$ is the design share of parameters.
• A module is considered sufficient if the sum of included parameter priors exceeds $\mathrm{BaselineScore}(M)$.
• This sufficiency model enables progressive enrichment (higher-salience fields first) and renders coverage auditable and cross-comparable.

This establishes a standardized quantitative foundation for evaluating completeness and for driving documentation effort (e.g., "fill high-prior gaps first").

4. Actionability, Comparison, and Cognitive Load Reduction

AI-generated factor cards, particularly via frameworks such as CRAI-MCF (Yang et al., 8 Oct 2025), offer several practical advantages over traditional cards:

• Direct Comparability: Fixed priors and baselines permit direct scoring and benchmarking of documentation sufficiency across heterogeneous models, facilitating like-for-like evaluation without manual normalization.
• Action Guidance: The sufficiency structure directly informs teams where to focus next (highest-utility, least-covered fields), making documentation a tractable incremental process rather than an unstructured narrative exercise.
• Gap Auditability: Missing fields translate into metricized gaps at both the module and parameter level, enabling systematic audit, improvement cycles, and regulatory compliance tracking.
• Modularity: Isolating updates to individual modules reduces maintenance effort (~38% average prose reduction reported), and improves practitioner acceptance as shown in empirical preference studies.
• Balanced Attention: Explicit mapping of modules to responsible AI principles (technical, ethical, operational) ensures no key domain is underreported—quantitative audited gaps enforce parsimony in all dimensions.

A summary of these improvements is provided below.

Aspect	Classical Model Cards / FactSheets	CRAI-MCF
Structure	Flat, prose/checklist	Hierarchical, eight-module, atomic factors
Comparability	Qualitative, inconsistent	Quantitative, standardized, cross-domain
Actionability	Lacks prioritization guidance	Prior-guided, modular sufficiency thresholds
Maintenance	Entire card, high redundancy	Modular, local updates, lower burden
Auditable gaps	Implicit or invisible	Quantifiable at parameter/module level

5. Application Domains and Extensions

While model documentation is a primary use case, AI-generated factor cards have broader applicability:

• Risk Documentation: Retrieval-augmented generation frameworks (e.g., RiskRAG (Rao et al., 11 Apr 2025)) leverage AI-generated factor cards to systematically surface and prioritize model- and context-specific risks, map mitigations, and enable situation-aware responsible adoption of AI.
• System Lifecycle Documentation: Hazard-Aware System Cards (HASC) (Sidhpurwala et al., 23 Sep 2025) extend the card paradigm to full system blueprints, aggregating not only technical configuration but also dynamic hazard logs, incident tracking identifiers (e.g., ASH IDs), and automatable machine-readable audit trails.
• Financial Factor Analysis: Machine learning frameworks can generate, filter, and select optimal sets of predictive factors—alpha factor "cards"—for portfolio construction and risk management (e.g., AlphaForge (Shi et al., 2024), NeuralFactors (Gopal, 2024), NNAFC (Fang et al., 2020)), leveraging neural architectures and quantitative selection criteria such as the Information Coefficient.
• HCI and Memory Cues: AI-generated cards can serve as reflective cues in personal memory systems (e.g., Treasurefinder (Jeung et al., 2024)), design cards for HCI recommendations (Shin et al., 2024), or even in impact communication and card-sorting UX studies (Bogucka et al., 26 Aug 2025, Kuric et al., 14 May 2025).

These cards are operationalized via modular ontologies, automated LLM-based retrieval and summarization (e.g., CardGen (Liu et al., 2024)), or hybrid manual-AI pipelines, and can be represented in machine-readable schemas for compliance and lifecycle management (e.g., RDF-based AI Cards (Golpayegani et al., 2024)).

6. Impact on Responsible AI Practice and Limitations

Properly constructed AI-generated factor cards enable precise, auditable, and standardized model documentation, directly addressing weaknesses observed in large-scale empirical studies—such as low fill-out rates in environmental impact/limitations sections (Liang et al., 2024), poor coverage of risk/fairness factors, and static, non-comparable reporting schemes in prevailing repositories.

Empirical evidence indicates that such rigorously structured cards improve discoverability, model selection, auditability, and even adoption (as measured by download growth upon card addition (Liang et al., 2024)). Furthermore, they facilitate compliance with regulatory and organizational standards (EU AI Act, ISO/IEC 42001), operationalize Value Sensitive Design principles, and provide the foundation for process automation.

However, coverage and balance remain an ongoing challenge; even under the CRAI-MCF schema, technical fields are more completely filled than governance or ethical dimensions, signifying persistent reporting asymmetries. Maintenance of high-granularity factor sets also introduces curation complexity and necessitates active tooling support.

7. Quantitative Formulations and Summary Table

The quantitative mechanisms that differentiate AI-generated factor cards are summarized mathematically:

• Parameter-level reference prior:

$s_i = \frac{f_i}{N}$

• Module sufficiency threshold:

$$\mathrm{BaselineScore}(M) = \left( \frac{O_M}{O_{\text{All}}} + \frac{A_M}{A_{\text{All}}} \right) \cdot \frac{S_M}{2}$$

where $S_M = \sum_{i \in M} s_i$.

Below is a consolidated summary distinguishing CRAI-MCF from traditional artifacts:

Feature	Traditional Model Cards	CRAI-MCF (AI-generated factor cards)
Structure	Flat, static, prose-heavy	Hierarchical, modular, atomic factors
Sufficiency	Qualitative, non-comparable	Quantitative, frequency-based, actionable
Cross-comparison	Difficult, ad hoc	Direct, prioritized scoring/ranking
Auditability	Weak, narrative	Parameter- and module-level quantification
Maintenance	High overhead, global rebalance	Modular, 38% less redundant representation
Operationality	Lacks update/prioritization tools	Prior-guided roadmap for progressive filling

• CRAI-MCF: Human-aligned AI Model Cards with Weighted Hierarchy Architecture (Yang et al., 8 Oct 2025)
• RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting (Rao et al., 11 Apr 2025)
• Hazard-Aware System Card (HASC): Blueprints of Trust: AI System Cards for End to End Transparency and Governance (Sidhpurwala et al., 23 Sep 2025)
• Model Cards for Model Reporting (Mitchell et al., 2018)
• AI Model Card Practices: What's documented in AI? Systematic Analysis of 32K AI Model Cards (Liang et al., 2024)
• Financial Factor Generation: AlphaForge (Shi et al., 2024), NeuralFactors (Gopal, 2024), NNAFC (Fang et al., 2020)
• Impact Assessment Card: Communicating Risks and Benefits of AI Uses (Bogucka et al., 26 Aug 2025)
• Card Sorting Simulator: Augmenting Design of Logical Information Architectures (Kuric et al., 14 May 2025)

For further technical definitions, implementation details, and open-source resources, consult the original cited works.