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Data & PerformanceAlgorithmic bias

Model bias

Systematic error or differential behavior that can arise from data, design, assumptions, objectives, implementation, or use. Bias assessment should be tied to the model's purpose, affected populations, decisions, and applicable legal requirements.

Last updated: Last reviewed by: Model Risk Directory editorial team

How the term is used in model risk management

Systematic error or differential behavior that can arise from data, design, assumptions, objectives, implementation, or use. Bias assessment should be tied to the model's purpose, affected populations, decisions, and applicable legal requirements. The exact implementation varies by institution, model type, risk rating, and governing framework. Use the linked regulatory pages and buyer guides below for scope-specific requirements.

Related glossary terms

Data qualityExplainabilityAdverse action

Guides and frameworks that use this term

GuideModel Risk ValidationModel risk validation is the independent testing of a specific model's conceptual soundness, ongoing performance, and outcomes, performed by staff or a third-party firm genuinely separate from that model's development. It is the 'effective challenge' function required under SR 26-2 and SR 11-7, distinct from broader model risk management program design.FrameworkMAS AI MRMThe MAS AI MRM paper reports good practices observed in a thematic review of selected banks. It covers AI governance, inventories, materiality assessment, data, development, independent validation, deployment, monitoring, change, generative-AI risks, and third-party models. It is supervisory guidance and observed practice, not a general statutory MRM rule.FrameworkOSFI E-23OSFI E-23 is Canada's enterprise-wide model risk management guideline for federally regulated financial institutions. The final 2025 guideline takes effect on 1 May 2027 and applies a risk-based approach across model identification, inventory, risk ratings, lifecycle governance, independent review, monitoring, third-party models, and decommissioning.