How can we effectively evaluate AI systems?

What are the main challenges in evaluating AI systems?

Some main challenges include bias in data, lack of transparency, and difficulty in measuring performance accurately.

How can we address bias in AI evaluations?

We can address bias by using diverse datasets, implementing fairness metrics, and regularly auditing AI systems.

What metrics should we use to evaluate AI performance?

Common metrics include accuracy, precision, recall, F1 score, and area under the ROC curve, depending on the application.

Can you suggest best practices for AI evaluation?

Best practices include continuous monitoring, involving domain experts, and using a combination of qualitative and quantitative methods.