Trustworthiness Characteristics are the set of interdependent properties defined by the NIST AI RMF that collectively describe the desired behavior and reliable performance of an AI system, forming the measurable dimensions against which AI risk is assessed and managed.

These characteristics provide a holistic and operational definition of what it means for an AI system to be "trustworthy." Rather than a single metric, trustworthiness is presented as a multi-faceted construct encompassing technical performance, ethical impact, and security posture. The NIST AI RMF identifies these characteristics as the foundational lens for the entire risk management process: they are the categories of concern to be mapped, the attributes to be measured, and the objectives to be achieved through risk management actions. They translate broad societal expectations into specific engineering and governance goals.

The NIST AI RMF core Trustworthiness Characteristics include:

Validity & Reliability: Ensuring the system performs accurately for its intended purpose (validity) and performs consistently under expected conditions (reliability).

Safety & Security: Protecting against conditions that can lead to physical, digital, or other harm (safety) and safeguarding against malicious manipulation or unauthorized access (security).

Accountability & Transparency: Ensuring clear assignment of responsibility for system outcomes (accountability) and providing appropriate disclosure of information about the system (transparency).

Explainability & Interpretability: Enabling users to understand the system's output and the reasoning process behind it.

Privacy: Enhancing protections for personal and sensitive information throughout the AI lifecycle.

Fairness & Bias Mitigation: Ensuring the system's outputs are just and equitable, avoiding unjustified disparate impacts on individuals or groups.

Regulatory Context: These characteristics are not a regulation themselves but a comprehensive schema that aligns with and underpins regulatory requirements. Each characteristic maps directly to obligations in laws like the EU AI Act (e.g., Fairness to Article 10, Safety to Article 15) and provides the detailed attributes that standards like ISO/IEC 42001 aim to safeguard through its management system controls.

Unified Framework for Evaluation: By adopting this shared vocabulary of characteristics, organizations, regulators, and developers can communicate clearly about AI system quality. They provide a common set of criteria for procurement, auditing, and certification, moving beyond vague claims of "ethical AI" to specific, verifiable assertions about system behavior.

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