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When AI goes wrong

Practical AI

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Navigating AI Liabilities and Incident Response

This chapter explores the unique challenges and liabilities associated with AI applications, contrasting them with traditional software issues. It emphasizes the need for robust testing and debugging practices for machine learning systems, while addressing the implications of algorithmic decision-making on individuals' lives. The importance of an AI incident response plan is discussed, alongside strategies for enhancing the reliability and accountability of AI models in real-world applications.

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