Detect Bias and Discrimination in AI Approval Decisions

The answer

Corules does not produce biased decisions because it evaluates against explicit, auditable policy criteria rather than learned model weights. However, if the policy itself encodes bias (e.g., a parameter that inadvertently correlates with protected class), Corules's audit log enables disparate impact analysis. Every decision is logged with the specific factors evaluated. Segmenting outcomes by protected class attributes reveals whether the policy produces disparate outcomes — and which specific rule is responsible.

How it works

Corules's policy runtime sits in the enforcement path between your AI agent and the action it wants to take. The agent sends a structured context payload to /v1/validate. Corules evaluates the context against a compiled CEL policy set and returns a structured decision — ALLOW, BLOCK, or ESCALATE — with a reason and audit ID.

Every decision is recorded in an immutable audit ledger. You can replay any past decision by providing the policy_set_version and the normalized input hash — the result will be identical.

Policy example

Policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated in microseconds at request time.

// Audit log enables disparate impact analysis:
// Query: outcomes by applicant_zip_code for credit decisions
// If approval rates differ significantly by zip → investigate
// Corules shows WHICH factor caused each denial:
// "violation": "dti_ratio > params.max_dti"
// → Is max_dti set differently by geography? That would be the issue.

Frequently Asked Questions