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Long-Term Monitoring and Performance Evaluation of AI Systems
Abstract
With AI increasingly used in healthcare, finance, and autonomous systems, ongoing monitoring is vital to ensure performance, fairness, and ethical compliance. Long-term oversight helps control risks, improve systems, and maintain trust. Monitoring frameworks detect issues like model drift, bias, or degradation caused by data shifts, user behavior, or environmental changes—prompting regular retraining. Beyond accuracy, key metrics include fairness, transparency, robustness, efficiency, and compliance. Explainability ensures stakeholders understand AI decisions, while robustness testing checks performance under varied inputs. AI observability tools with logging and analytics detect abnormal behavior early. Human oversight complements automated systems to catch subtle issues and ensure social responsibility. Monitoring many complex AIs remains a challenge, demanding better tools for transparency and adaptation. Regular testing, human involvement, and governance ensure AI stays effective, ethical, and lawful. These practices support responsible AI development and foster user trust.
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