---
---
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.