System Card

A structured transparency document detailing a single machine learning model's intended use, performance metrics, evaluation data, and known limitations. Unlike a System Card, a Model Card focuses narrowly on the model artifact itself, not the surrounding user interface, safety mitigations, or operational context. Standardized by Google in 2018, it serves as the atomic unit of model reporting.

• Reports metrics like accuracy, F1 score, and fairness evaluations across demographic slices
• Documents training data composition and known biases
• Specifies out-of-scope use cases to prevent unsafe deployment

A standardized document outlining a dataset's motivation, composition, collection process, and recommended uses. Inspired by electronics datasheets, this artifact provides the data provenance context that both Model Cards and System Cards depend on. It answers critical questions about who collected the data, under what conditions, and for what purpose.

• Documents annotation protocols and inter-rater reliability
• Discloses personally identifiable information (PII) presence and consent mechanisms
• Provides the raw material lineage that feeds into system-level safety evaluations

A centralized, searchable inventory cataloging an organization's deployed automated systems, their risk classifications, and associated transparency artifacts. A System Card is the detailed disclosure linked to each registry entry. The registry provides the governance scaffolding—enabling auditors to quickly locate all high-risk systems and verify that each has a current, complete System Card on file.

• Maps each system to regulatory categories under the EU AI Act
• Tracks System Card version history and last review dates
• Enables enterprise-wide compliance dashboards for CTOs and auditors

A formal, machine-readable record detailing the complete supply chain of an AI system, including model architecture, training data provenance, software dependencies, and hardware requirements. A System Card references the AI BOM as its technical appendix—the BOM provides the granular component inventory while the System Card provides the narrative safety evaluation.

• Extends the SBOM (Software Bill of Materials) concept to AI-specific assets
• Captures model weights, fine-tuning checkpoints, and evaluation harness versions
• Essential for vulnerability management and supply chain attestation

A systematic process for identifying and evaluating the potential positive and negative effects of an AI system on all affected parties—end-users, operators, and society. A System Card incorporates the findings of this assessment into its safety evaluation section, documenting how identified harms are mitigated through design choices, guardrails, and human oversight mechanisms.

• Maps harms across privacy, fairness, safety, and autonomy dimensions
• Documents the participatory methods used to gather stakeholder input
• Provides the ethical rationale for residual risk acceptance decisions

The automated, real-time verification of AI systems against evolving regulatory standards using policy-as-code enforcement. A System Card is not a static document—it must be continuously updated as models drift, data distributions shift, and regulations change. Continuous compliance monitoring triggers System Card revision workflows when anomalies are detected.

• Detects concept drift and data drift that invalidate prior safety claims
• Automates evidence collection for audit trail immutability
• Integrates with CI/CD pipelines to gate deployments on System Card currency