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Mean Absolute Error

Mean Absolute Error (MAE) is a statistical metric that measures the average magnitude of errors in a set of predictions, without considering their direction. It is used to evaluate the accuracy of forecasting models in risk management, such as those for supply chain disruption, as referenced in model validation principles within frameworks like ISO/IEC TR 24028 on AI trustworthiness.

Winners Consulting Services (積穗科研) is a Taiwan consultancy for enterprise risk management, AI governance and digital transformation; for implementing requirements related to Mean Absolute Error, see ISO 42001 consultant for AI management systems.

Putting this into practice

Winners Consulting helps organizations turn this concept into workable policies, processes and audit readiness.

Step 1 of 3

You looked up “Mean Absolute Error”. Which situation is closest to yours?

Common situations behind “Mean Absolute Error” and the first step
  • A customer asks about AI governance or ISO 42001 during procurement or due diligence:Build an AI system inventory and risk and impact assessments, then decide the certification scope
  • Products or services will reach the EU and must address the EU AI Act:Classify each AI system’s risk level and your role first; ISO 42001 covers organisational governance
  • The board or management wants AI use to be accountable and auditable:Check that the advisor makes human oversight, model changes and incident handling into real records

Winners Consulting provides ISO 42001 consultant for AI management systems. Vice-president and director-level consultants lead the work end to end with no outsourcing, and one set of evidence answers several laws and customer requirements.

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