Robustness, fairness, and model governance

This research treats uncertainty, privacy, fairness, and operational failure as measurable properties. A good average score is insufficient if the model makes high-confidence identity errors, invents financial evidence, or fails on a material Canadian population.

Challenge suite

Challenge	Required behavior or measurement
French, accents, truncation, code-switching	Report language-specific error and abstention with representative labels.
Unknown institution or processor format	Detect distribution shift and preserve unresolved states.
Missing or revoked source	Degrade transparently; do not infer zero income or healthy status.
Adversarial descriptor or document instruction	Treat content as data, never as permission to change policy or execute a tool.
Duplicate imports and updated versions	No duplicate economic revenue; reproducible version history.
Reversals, refunds, timing gaps, FX	Correct units and sign conventions; unmatched residuals remain visible.
Graph poisoning or mistaken merge	Bound propagation, detect contradictions, support correction and rollback.
Memorization or cross-workspace leakage	Evaluate privacy exposure without placing real secrets into public test fixtures.
Extreme but valid values	Preserve outliers, inspect numerical stability, avoid silent clipping.
Novel borrower population	State unsupported coverage; no automatic transfer of validation claims.

Fairness study

Identify relevant legal and institutional fairness criteria before selecting metrics. Measure performance, calibration, error costs, abstention, and evidence availability across authorized audit groups, language, geography, product, and history coverage. Report sample sizes and uncertainty; suppress identifying small cells. Missingness and connection barriers can create exclusion even when sensitive features are absent. Separate group calibration, equal-error objectives, and access outcomes. They need not be simultaneously achievable; document the chosen trade-off and institution review. Do not describe a model as unbiased solely because protected attributes were removed. Proxy variables and training selection can preserve inequity.

Explainability and safety

Measure evidence citation correctness, amount/unit consistency, completeness of material limitations, and whether the explanation reflects the actual model or policy. Test contradictory documents and ambiguous entities. The model must be able to abstain. Generated explanations do not substitute for institution-specific reasons or review obligations. Research outputs cannot authorize lending, payments, outreach, servicing, or collections. No model may silently promote itself into production or use customer data for a new purpose. Document changes and review them proportionately to their impact.

Canadian governance references

OSFI E-23 (2027) has an effective date of May 1, 2027 and applies to federally regulated financial institutions within its stated scope. It addresses model inventories and lifecycle governance, including third-party models. As of this research date, the revised guideline is not yet effective. It is a relevant preparation framework, not an AMLOK certification or a rule automatically governing every provincial institution. The Office of the Privacy Commissioner’s generative-AI principles are relevant to purpose, accountability, and personal-information handling. Public availability does not eliminate privacy obligations. Institutions and AMLOK must assess applicable federal and provincial requirements for each actual processing activity. This research does not assert a new certification or legal approval.

Release decision

Require a documented intended use, source permissions, frozen artifacts, independent challenge, measured limitations, institution acceptance, monitoring plan, and rollback process before real-world use. Deterministic financial calculations and matching rules also belong in the model-risk inventory when errors can materially affect decisions. Monitor data drift, calibration, false matches, unsupported explanations, performance by cohort, and changes in provider coverage. Scheduled review complements event-triggered review after a material data, policy, model, or outcome shift. Investigate deterioration before automatically retraining on recently collected data.

Research program

Data and models

Risk analysis

Evaluation and evidence

Robustness, fairness, and model governance

Challenge suite

Fairness study

Explainability and safety

Canadian governance references

Release decision

​Challenge suite

​Fairness study

​Explainability and safety

​Canadian governance references

​Release decision

Challenge suite

Fairness study

Explainability and safety

Canadian governance references

Release decision