What are some common challenges in testing AI systems?

What are some common challenges in testing AI systems?

Some common challenges include data quality, model interpretability, and ensuring fairness in AI outcomes.

How do you ensure data quality during testing?

Ensuring data quality involves validating data sources, cleaning data, and using diverse datasets to avoid bias.

What is model interpretability and why is it important?

Model interpretability refers to understanding how AI models make decisions, which is crucial for trust and accountability.

Can you give examples of fairness issues in AI?

Sure! Examples include biased hiring algorithms and facial recognition systems that misidentify certain demographics.