File size: 2,811 Bytes
e18e9b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
---
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. |