What are the main challenges in AI testing?

What are the main challenges in AI testing?

Some key challenges include data quality, model interpretability, and ensuring robustness against adversarial attacks.

Can you explain why data quality is so important?

Data quality is crucial because poor data can lead to inaccurate models, which may produce unreliable predictions.

What about model interpretability?

Model interpretability helps stakeholders understand how decisions are made, which is vital for trust and compliance.

How do adversarial attacks affect AI systems?

Adversarial attacks can manipulate AI models by introducing subtle changes to input data, leading to incorrect outputs.