|
|
--- |
|
|
--- |
|
|
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. |
