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--- |
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--- |
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license: cc-by-4.0 |
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task_categories: |
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- tabular-classification |
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- risk-assessment |
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tags: |
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- GRC |
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- AI-governance |
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- risk-management |
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- quantitative-analysis |
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- finops |
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language: |
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- en |
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size_categories: |
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- n<1K |
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--- |
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# AI Loss Taxonomy & Financial Impact Framework |
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## Dataset Summary |
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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. |
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It categorizes losses across **Compliance, IT/Technical, Operational, and Revenue** domains, providing a clear vocabulary for CFOs, CROs, and CISOs to discuss AI risk. |
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## Author & Attribution |
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This taxonomy was developed and curated by: |
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**Prof. Hernan Huwyler, MBC, CPA** |
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* Academic Director |
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* AI GRC Director |
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*Please attribute Prof. Hernan Huwyler when utilizing this taxonomy for academic or commercial risk frameworks.* |
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## Dataset Structure |
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The dataset contains the following fields: |
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* **Domain:** The business area where the financial loss materializes (e.g., *Compliance*, *Revenue*). |
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* **Loss Name:** Standardized terminology for the specific type of financial loss. |
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* **Explanation:** Detailed definition of the loss, including direct and indirect costs. |
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* **Source:** Attribution field. |
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## Use Cases |
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### 1. Quantitative Risk Analysis (FAIR) |
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Use this dataset to define the **Loss Magnitude** side of your risk equation. |
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* *Scenario:* An AI model exhibits bias (Threat). |
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* *Loss Calculation:* Use "Regulatory Fines" + "Reputation Damage" + "Algorithm Remediation" from this dataset to calculate the Total Loss Exposure (TLE). |
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### 2. AI ROI & Cost-Benefit Analysis |
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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. |
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### 3. Insurance & Liability |
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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." |
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## Example Data |
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| Domain | Loss Name | Explanation | |
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|---|---|---| |
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| **Compliance** | Regulatory Fines | Penalties for violating AI regulations like EU AI Act or privacy laws. | |
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| **IT/Technical** | Algorithm Remediation | Engineering costs to retrain models that produce biased or inaccurate predictions. | |
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| **Revenue** | Customer Churn | Lost revenue from customers leaving after negative AI experiences. | |
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## Citation |
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If you use this dataset in research or tooling, please cite: |
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> Huwyler, H. (2025). AI Loss Taxonomy. Hugging Face Datasets. |
