disaggregated-evaluation

Name: disaggregated-evaluation
Availability: InStock
Author: DTMC-marketplace

Evaluate AI model performance across demographic subgroups to detect bias.

2|Updated Jan 15, 2026

Search Tags:#bias detection #model performance #eu ai act #ai fairness #disaggregated evaluation #demographic subgroups

Ready to run

npx skills add https://github.com/DTMC-marketplace/governance --skill disaggregated-evaluation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: disaggregated-evaluation
Source: https://github.com/DTMC-marketplace/governance/tree/main/skills/disaggregated-evaluation
Command: npx skills add https://github.com/DTMC-marketplace/governance --skill disaggregated-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the critical need to evaluate AI model performance not just overall, but specifically across different demographic subgroups, ensuring fairness and identifying potential biases.

Core Features & Use Cases

Disaggregated Performance Metrics: Analyze model accuracy, precision, recall, etc., for various demographic segments.
Bias Detection: Identify performance disparities that may indicate unfair treatment of certain groups.
Compliance Assessment: Helps meet regulatory requirements like the EU AI Act's Art. 10 and Art. 15 by providing evidence of fair AI system operation.
Use Case: A financial institution uses this Skill to check if their loan approval model performs equally well for applicants of different ethnicities and genders, flagging any significant performance gaps.

Quick Start

Use the disaggregated-evaluation skill to assess model performance across different demographic subgroups.

Frequently Asked Questions about disaggregated-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect bias in AI model performance across demographic subgroups?▼

Disaggregated evaluation detects bias in AI models by analyzing performance metrics across demographic subgroups to identify significant disparities. This process compares accuracy and precision variations between different population segments to flag unfair treatment.

What is disaggregated evaluation for AI fairness?▼

Disaggregated evaluation for AI fairness is the process of assessing model performance metrics like accuracy and recall for specific demographic segments. It identifies performance gaps that indicate potential bias against certain groups.

How do I check AI fairness for EU AI Act compliance?▼

You check AI fairness for EU AI Act compliance by evaluating disaggregated model performance across demographic subgroups. This provides documented evidence of risk assessment and fair operation required under Article 10 and Article 15.

Can I use disaggregated evaluation to measure loan approval model bias?▼

Yes, you can use disaggregated evaluation to measure loan approval model bias by segmenting applicant data into ethnicities and genders. This flags significant performance gaps in approval predictions for different demographic groups.

Do I need data analysis tools to evaluate model performance by demographic subgroups?▼

Yes, you need data analysis tools to evaluate model performance by demographic subgroups. The evaluation requires external tools for data segmentation and analysis to calculate performance metrics for each group.