Interpretability
Degree to which humans can understand AI decisions.
How the laws define this
Both sources agree that interpretability fundamentally involves enabling a human or stakeholder to perceive and grasp how an artificial intelligence system's inputs and internal processes or mechanics lead to specific outputs or decisions. The key divergence lies in the scope and operational purpose attributed to the concept. The Egyptian Charter for Responsible Artificial Intelligence connects interpretability directly to practical oversight functions, stating that it serves to support meaningful explanations, facilitate error analysis, and enable informed human oversight and trust. Conversely, ISO/IEC TS 6254:2025 — Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of machine learning (ML) models and artificial intelligence (AI) systems focuses on intrinsic transparency, defining interpretability through a deeper grasp of model parameters and internal decision-making logic. While the Egyptian Charter emphasizes the user-facing and governance outcomes of understanding a system, the ISO/IEC technical specification isolates the internal mechanics of the model itself.
Synthesised from the 2 statutory definitions below. Each one is quoted in full, with its source.
How jurisdictions differ
What is distinctive in each definition, binding law in force first.
| Jurisdiction | Instrument | Status | What is distinctive |
|---|---|---|---|
| ISO | ISO/IEC TS 6254:2025 — Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of machine learning (ML) models and artificial intelligence (AI) systems | In Force | Emphasizes intrinsic transparency of a model's operations, including how inputs lead to outputs and the significance of various model parameters. |
| Egypt | Egyptian Charter for Responsible Artificial Intelligence | Adopted | Emphasizes support for meaningful explanations, error analysis, and informed human oversight and trust. |
What it triggers
Definitions (2)
The property of AI systems that allows stakeholders to perceive and understand how inputs and internal processes lead to specific outputs or decisions. Interpretability supports meaningful explanations, facilitates error analysis, and enables informed human oversight and trust.
Interpretability, often closely related to explainability, denotes the extent to which a human can grasp the internal mechanics of an AI system, including how inputs lead to outputs and the significance of various model parameters. It focuses on the intrinsic transparency of a model's operations, allowing for a deeper understanding of its decision-making logic.
Not to be confused with
Explicability focuses on making AI outputs interpretable to stakeholders, while interpretability focuses on understanding the model's internal logic.
Algorithmic explainability emphasizes understanding how an AI system reaches decisions, while interpretability focuses on the model's intrinsic transparency.
Explicability/interpretability emphasizes explaining model operations and outputs, while interpretability focuses on intrinsic transparency.
Questions and answers
What is distinctive about interpretability in ISO/IEC TS 6254:2025?
It denotes the extent to which a human can grasp the internal mechanics of an AI system, including how inputs lead to outputs and the significance of various model parameters.
What is distinctive about interpretability in the Egyptian Charter for Responsible Artificial Intelligence?
It supports meaningful explanations, facilitates error analysis, and enables informed human oversight and trust.
How does interpretability relate to understanding AI systems?
It allows stakeholders to perceive and understand how inputs and internal processes lead to specific outputs or decisions.
Related Terms
Explicability
Making AI outputs interpretable to stakeholders....
Algorithmic Explainability
Ability to understand how an AI system reaches decisions....
Explainability/interpretability
Extent to which a model's operation and outputs can be meaningfully described....
Explicability of AI
Requirement that AI decisions can be interpreted and explained....
model transparency
Clarity about a model's operation, inputs, outputs and limitations....