license: cc-by-4.0
task_categories:
- tabular-classification
- text-generation
tags:
- ISO-25059
- quality-assurance
- GRC
- ai-governance
- non-functional-requirements
language:
- en
size_categories:
- n<1K
AI System Quality Objectives (ISO/IEC 25059:2023)
Dataset Summary
This dataset establishes a standardized taxonomy of Quality Objectives for AI systems, based on ISO/IEC 25059:2023 (Quality models for AI systems). It adapts the classic software quality model (ISO 25010) specifically for Artificial Intelligence contexts, covering domains such as Transparency, Robustness, Bias Mitigation, and Intervenability.
It is designed to help AI Architects and GRC leaders define "Non-Functional Requirements" (NFRs) and control frameworks.
Author & Attribution
This framework was curated and adapted by: Prof. Hernan Huwyler, MBC, CPA
- Academic Director
- AI GRC Director
This dataset synthesizes the ISO/IEC 25059 standard with actionable guidance for implementation.
Dataset Structure
The dataset contains the following fields:
- Domain: The high-level quality category (e.g., Functional Suitability, Usability, Security).
- Quality Characteristic: The specific attribute being measured (e.g., Unexplainability, Functional Correctness).
- Definition: The formal ISO-aligned definition of the characteristic.
- Guidance: Actionable controls, testing strategies, and references (e.g., NIST AI RMF, EU AI Act) to achieve the objective.
Use Cases
1. AI Control Framework Design
GRC teams can import this list to create a control baseline.
- Example: For a high-risk AI system, select "Societal and ethical risk mitigation" and implement the suggested "Impact Assessments."
2. Non-Functional Requirements (NFR) Gathering
Engineering teams use this to ensure they are building the right system.
- Prompt: "Does this system require Intervenability (Human-in-the-loop)? If so, we must design pause-functions."
3. Auditing & Compliance
Auditors can use this checklist to verify if an AI system meets quality standards required by the EU AI Act (which heavily overlaps with ISO 25059).
Example Data
| Domain | Quality Characteristic | Guidance |
|---|---|---|
| Reliability | Robustness | Test with noisy, out-of-distribution, and adversarial inputs. Use adversarial training. |
| Usability | Transparency | Implement Explainable AI (XAI) techniques (e.g., SHAP). Document model purpose. |
| Security | Intervenability | Design human-in-the-loop processes. Ensure the system can be paused or stopped safely. |
Citation
If you use this dataset in research or tooling, please cite:
Huwyler, H. (2024). AI Quality Objectives (ISO/IEC 25059). Hugging Face Datasets.