Framework (in Trustworthy AI)
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
A structured set of principles, processes, and controls for governing AI systems, such as the NIST AI RMF, the EU's Trustworthy AI guidelines, or ISO/IEC 42001.
What is a Framework?
In trustworthy AI, a framework is a structured set of principles, processes, and controls an organization follows to govern its AI systems, so that decisions about risk, fairness, and oversight are deliberate rather than improvised.
In software engineering, the same word names pre-built code structures such as TensorFlow or PyTorch; that is a different thing entirely.
How a Framework Works
A framework does not say which model to build. It says which questions must be answered before and after building one, who is accountable for the answers, and what evidence gets kept. It turns a vague commitment like “our AI is fair” into named activities with owners, schedules, and records.
Most frameworks are voluntary, but they carry weight anyway. Regulators and customers increasingly expect organizations to follow one, and a shared framework gives an AI audit or an AI assessment a standard to judge against.
Types of Frameworks
- Risk-management frameworks: The NIST AI Risk Management Framework, released in January 2023, organizes AI risk management into four functions: Govern, Map, Measure, and Manage.
- Ethics guidelines: The EU High-Level Expert Group’s 2019 Ethics Guidelines for Trustworthy AI set seven requirements, from human oversight to accountability, and shaped the EU AI Act.
- Management-system standards: ISO/IEC 42001 specifies requirements for an AI management system, and organizations can be certified against it, the way ISO 27001 works for information security.
Example of a Framework
An insurer adopts the NIST AI RMF before deploying a claims-triage model, and the four functions dictate the work. Govern: the board approves an AI policy and names an owner accountable for the system.
Map: the team documents the use case, who is affected by a mis-routed claim, and where the risks sit, from skewed historical data to drift. Measure: it tests accuracy, compares error rates across customer groups, and probes edge cases.
Manage: the worst risks get mitigations, a human reviews low-confidence cases, and monitoring is scheduled. None of this told the insurer how to build the model. It told them what a defensible deployment must have produced along the way, which is exactly what a framework is for.
Related AI terms: AI Governance · Risk Management · Trustworthy AI · AI Audit · EU AI Act
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About