What are the main challenges in testing AI systems?

What are the main challenges in testing AI systems?

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

How can we ensure data quality for AI testing?

Ensuring data quality involves rigorous data validation, cleaning processes, and continuous monitoring of data sources.

What does model interpretability mean?

Model interpretability refers to the ability to understand and explain how an AI model makes its decisions.

Can you give examples of fairness issues in AI?

Sure! Examples include biased training data leading to discriminatory outcomes and lack of representation in datasets.