ailedger-detection
What it is
A Python library providing statistical primitives for detecting bias and drift in AI decision events. It implements disparate impact ratio, statistical parity difference, and model drift (PSI) calculations anchored to EEOC and FDIC/OCC regulatory standards.
The problem it solves
Lack of standardized, auditable statistical tools for detecting bias and drift in AI decision-making that align with specific regulatory frameworks like EEOC and FDIC.
What you could use it for
- Auditing AI hiring models for disparate impact against EEOC four-fifths rule
- Monitoring credit decision models for population stability drift per FDIC guidelines
- Verifying statistical parity in automated decision systems for regulatory compliance
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.