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Effective management and oversight are crucial for the success and reliability of AI systems.

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In this section, you will learn about the continuous monitoring and validation of AI systems, ensuring

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these systems operate as intended and remain aligned with their design objectives.

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Exploring post-hoc testing for AI system accuracy and effectiveness, you will delve into methodologies

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that assess performance and identify potential shortcomings after deployment.

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Understanding the complexities of managing automation bias in AI systems is essential, as this lesson

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will guide you on recognizing and mitigating unintended biases that could influence decision making

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processes.

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Model versioning and updates are vital for maintaining the relevance and functionality of AI applications.

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Best practices in this domain will be covered, providing you with strategies to efficiently manage

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different versions and updates of AI models.

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The challenge of managing third party risks post-deployment will also be addressed, offering insights

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into how to safeguard your AI systems from vulnerabilities introduced by external vendors or partners.

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Reducing unintended use and downstream harm in AI systems is another critical topic where you will learn

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to anticipate and prevent adverse consequences that may arise from the misuse of AI technologies.

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Finally, planning for AI system deactivation and system sunset will be discussed.

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This lesson will equip you with the methodologies to responsibly phase out AI systems, ensuring a smooth

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transition and minimal disruption.

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By the end of this section, you will gain a comprehensive understanding of these key aspects, empowering

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you to effectively oversee and maintain the integrity, performance and ethical standards of AI systems

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throughout their life cycle.