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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: Loss Name
-    dtype: string
-  - name: Explanation
-    dtype: string
-  - name: Source
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3373
-    num_examples: 15
-  download_size: 4779
-  dataset_size: 3373
----
+---
+---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- risk-assessment
+tags:
+- GRC
+- AI-governance
+- risk-management
+- quantitative-analysis
+- finops
+language:
+- en
+size_categories:
+- n<1K
+---
+
+# AI Loss Taxonomy & Financial Impact Framework
+
+## Dataset Summary
+
+This dataset provides a structured taxonomy of **AI Loss Events**, designed to help organizations quantify the financial impact of Artificial Intelligence failures. Unlike threat lists which focus on *probability*, this taxonomy focuses on *magnitude* (impact), enabling Quantitative Risk Analysis (e.g., FAIR methodology) for AI systems.
+
+It categorizes losses across **Compliance, IT/Technical, Operational, and Revenue** domains, providing a clear vocabulary for CFOs, CROs, and CISOs to discuss AI risk.
+
+## Author & Attribution
+
+This taxonomy was developed and curated by:
+**Prof. Hernan Huwyler, MBC, CPA**
+*   Academic Director
+*   AI GRC Director
+
+*Please attribute Prof. Hernan Huwyler when utilizing this taxonomy for academic or commercial risk frameworks.*
+
+## Dataset Structure
+
+The dataset contains the following fields:
+
+*   **Domain:** The business area where the financial loss materializes (e.g., *Compliance*, *Revenue*).
+*   **Loss Name:** Standardized terminology for the specific type of financial loss.
+*   **Explanation:** Detailed definition of the loss, including direct and indirect costs.
+*   **Source:** Attribution field.
+
+## Use Cases
+
+### 1. Quantitative Risk Analysis (FAIR)
+Use this dataset to define the **Loss Magnitude** side of your risk equation.
+*   *Scenario:* An AI model exhibits bias (Threat).
+*   *Loss Calculation:* Use "Regulatory Fines" + "Reputation Damage" + "Algorithm Remediation" from this dataset to calculate the Total Loss Exposure (TLE).
+
+### 2. AI ROI & Cost-Benefit Analysis
+When calculating the Return on Investment for AI, use the "Operational" and "IT/Technical" loss categories to estimate potential "Development Waste" and "Infrastructure Overruns" to calculate a risk-adjusted ROI.
+
+### 3. Insurance & Liability
+This taxonomy assists insurance actuaries and corporate risk managers in defining coverage limits for AI liability policies by identifying specific cost drivers like "Legal Response" vs "Data Regeneration."
+
+## Example Data
+
+| Domain | Loss Name | Explanation |
+|---|---|---|
+| **Compliance** | Regulatory Fines | Penalties for violating AI regulations like EU AI Act or privacy laws. |
+| **IT/Technical** | Algorithm Remediation | Engineering costs to retrain models that produce biased or inaccurate predictions. |
+| **Revenue** | Customer Churn | Lost revenue from customers leaving after negative AI experiences. |
+
+## Citation
+If you use this dataset in research or tooling, please cite:
+> Huwyler, H. (2025). AI Loss Taxonomy. Hugging Face Datasets.