What are the main challenges in AI testing and integration?
The main challenges include data quality, model interpretability, and ensuring compliance with regulations.
How does data quality affect AI testing?
Data quality impacts the accuracy and reliability of AI models, leading to potential failures in real-world applications.
What is model interpretability?
Model interpretability refers to how well we can understand and explain the decisions made by AI models.
Why is compliance important in AI integration?
Compliance ensures that AI systems adhere to legal and ethical standards, protecting users and organizations.
Can you give examples of regulations affecting AI?
Sure! Examples include GDPR in Europe and the AI Act, which focus on data protection and ethical AI use.
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