What are some common mistakes in AI evaluation?

What are some common mistakes in AI evaluation?

Some common mistakes include not using diverse datasets, ignoring bias, and failing to validate results.

Can you explain why using diverse datasets is important?

Diverse datasets help ensure that the AI model performs well across different scenarios and populations.

What about bias? How does it affect AI evaluation?

Bias can lead to unfair outcomes and misrepresentation, skewing the evaluation results and affecting trust.

How can we mitigate these mistakes during evaluation?

Mitigation strategies include thorough testing, using representative samples, and continuous monitoring for bias.