What are some common challenges in evaluating AI systems?

What are some common challenges in evaluating AI systems?

Some common challenges include data quality, bias in algorithms, and the complexity of measuring performance.

How can we address bias in AI evaluations?

Addressing bias involves using diverse datasets, regular audits, and implementing fairness metrics during evaluation.

What metrics are best for evaluating AI performance?

Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC, depending on the application.

Can you give examples of real-world evaluation challenges?

Sure! Challenges include evaluating AI in healthcare for patient outcomes and in finance for fraud detection.