What are the best practices for evaluating AI systems?

What are the best practices for evaluating AI systems?

Best practices for evaluating AI systems include defining clear evaluation criteria, using diverse datasets, and involving domain experts.

How important is it to have diverse datasets?

Diverse datasets are crucial as they help ensure the AI performs well across different demographics and scenarios.

What role do domain experts play in the evaluation process?

Domain experts provide valuable insights and context, helping to assess the AI's relevance and effectiveness in real-world applications.

Can you suggest some tools for AI evaluation?

Sure! Some popular tools include TensorFlow Model Analysis, MLflow, and Weights & Biases for tracking and evaluating models.

What are common pitfalls to avoid during AI evaluation?

Common pitfalls include overfitting to the evaluation dataset, ignoring ethical considerations, and failing to update evaluation criteria as technology evolves.