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Effective auditing and monitoring of AI systems are crucial for ensuring they operate as intended and

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do not inadvertently cause harm.

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In this section, you will learn about the methods and tools for conducting thorough AI audits.

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These foundational skills will help you systematically evaluate AI models for biases, performance,

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and compliance with regulatory standards.

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By mastering these tools, you will be better equipped to identify areas for improvement and maintain

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the integrity of AI systems.

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Next, we will delve into evaluating AI's societal impact, exploring various metrics and approaches

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to understand how AI technologies affect different communities and sectors.

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You'll discover methods for quantifying both positive and negative impacts, equipping you with the

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knowledge to assess AI systems comprehensively and responsibly.

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Tracking AI system performance.

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Post-deployment is another critical skill you will acquire.

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This lesson will cover techniques for continuous monitoring, allowing you to detect performance, drift,

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and other issues early.

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Understanding how to effectively track and analyze AI performance ensures that the systems remain accurate

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and reliable over time.

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Once problems are identified, remediating AI system failures and negative impacts becomes imperative.

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You will explore strategies for addressing these failures, focusing on both technical fixes and policy

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adjustments to mitigate adverse consequences and restore trust in the AI systems.

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Communicating risks associated with AI systems is equally important.

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You will learn how to report and communicate these risks effectively to various stakeholders, ensuring

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transparency and fostering informed decision making.

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This will include best practices for crafting clear and concise risk reports.

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Creating ethical AI impact reports for stakeholders will also be a key topic.

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This lesson will guide you through the process of documenting AI impacts in a way that is both ethical

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and informative, helping stakeholders understand the broader implications of AI deployments.

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Finally, preparing AI systems for continuous evaluation and updates will round out your learning.

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This involves establishing protocols for ongoing assessment and iterative improvements, ensuring that

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AI systems evolve responsibly alongside technological advancements and societal needs.

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By the end of this section, you will have a comprehensive toolkit for auditing, evaluating, and communicating

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the performance and impact of AI systems, empowering you to contribute positively to the field of artificial

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intelligence.