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README.md

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----
-license: mit
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: Domain
-    dtype: string
-  - name: Quality Characteristic
-    dtype: string
-  - name: Definition
-    dtype: string
-  - name: Guidance
-    dtype: string
-  - name: Standard
-    dtype: string
-  - name: Curator
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 12095
-    num_examples: 49
-  download_size: 9211
-  dataset_size: 12095
----
+---
+---
+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.