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.