How can we prevent AI failures?

What are some common causes of AI failures?

Common causes include data quality issues, lack of proper training, and algorithmic biases.

How can we improve data quality for AI?

Improving data quality can be achieved by ensuring accurate data collection, cleaning, and validation processes.

What role does testing play in preventing AI failures?

Testing is crucial; it helps identify issues before deployment and ensures the AI performs as expected in real-world scenarios.

Can you suggest best practices for AI development?

Best practices include iterative development, continuous monitoring, and involving diverse teams to mitigate biases.