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Navigating the complexities of AI projects demands a clear understanding of their scope and associated

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risks.

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In this section, you will learn how to identify key objectives when scoping AI projects, ensuring

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alignment with organizational goals and stakeholder expectations.

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Recognizing and mapping both internal and external threats is critical for robust AI risk management.

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You will explore methodologies for identifying these risks, including potential biases, compliance

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issues, and security threats.

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Developing effective risk mitigation strategies is essential to minimize potential adverse outcomes.

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This section will guide you through creating comprehensive risk mitigation plans tailored to AI projects,

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emphasizing proactive measures and contingency planning.

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Constructing a harms matrix for AI risk assessment is another crucial skill you will acquire, facilitating

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the evaluation of diverse risk factors and their potential impact.

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Conducting algorithm impact assessments allows for a deeper understanding of how AI models affect various

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stakeholders.

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You will engage with techniques to assess and document these impacts systematically.

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Engaging stakeholders in AI risk management is paramount for transparent and inclusive decision making

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processes.

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This section will provide strategies for effective stakeholder communication and involvement.

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Data provenance, lineage, and accuracy are foundational to the integrity of AI systems.

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You will delve into best practices for tracking data origins, ensuring data quality, and maintaining

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transparency throughout the data lifecycle.

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By the end of this section, you will be equipped with the knowledge and tools to manage AI projects

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effectively, mitigate risks, and ensure ethical and responsible AI deployment.