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Jira, ServiceNow
Model providers
Anthropic, OpenAI
Agent tools
Copilot Studio, Claude Enterprise
Development environments
Azure AI Foundry, Amazon Bedrock, Databricks

Glossary

Fairness Metrics

Fairness Metrics

Fairness Metrics

Quantitative measures (e.g., demographic parity, equalized odds) used to evaluate how fair an AI model’s predictions are across groups.

Quantitative measures (e.g., demographic parity, equalized odds) used to evaluate how fair an AI model’s predictions are across groups.

Updated

Provide objective criteria to detect and monitor group-based outcome disparities. Common metrics include: Demographic Parity (equal positive-prediction rates), Equalized Odds (equal true/false positive rates), and Calibration (predicted risk matches observed outcomes). Governance frameworks mandate selecting appropriate metrics for each use case and tracking them continuously to enforce fairness SLAs.

Example

A university’s predictive-admissions model reports demographic parity differences quarterly. When female applicants’ positive-prediction rates fall below 95% of male applicants, an alert triggers a fairness review. The team adjusts decision thresholds to meet the 0.8 parity rule and documents the change in the fairness dashboard.

Govern the AI you actually use.

Govern the AI you actually use.

Govern the AI you actually use.

Start with what’s in use.

Start with what’s in use.

Start with what’s in use.

Book a demo and we’ll show you how Enzai finds the AI in use, gives each system an owner and keeps the record.

Book a demo and we’ll show you how Enzai finds the AI in use, gives each system an owner and keeps the record.