Post-Training Model Auditing

Post-Training Model Auditing refers to the comprehensive evaluation of machine learning models after they have been trained. This process involves assessing model performance, detecting biases, ensuring compliance with regulatory standards, and verifying the integrity of predictions. By analyzing factors such as accuracy, fairness, and robustness, organizations can optimize their AI systems, enhance decision-making, and mitigate risks. Effective post-training auditing is crucial for maintaining ethical AI practices and ensuring that models align with business objectives and user expectations. Implementing rigorous auditing procedures strengthens trust and accountability in AI technologies.

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