What are the main challenges in AI explainability?

What are the main challenges in AI explainability?

The main challenges include complexity of models, lack of standard metrics, and the trade-off between accuracy and interpretability.

Can you explain why model complexity is a challenge?

Model complexity makes it difficult to understand how decisions are made, as more complex models often act like 'black boxes'.

What are some methods to improve explainability?

Methods include using simpler models, feature importance analysis, and visualization techniques to make outputs more interpretable.

Are there any regulations regarding AI explainability?

Yes, regulations like GDPR in Europe emphasize the need for explainability in automated decision-making processes.

How does explainability impact user trust in AI?

Explainability enhances user trust by providing transparency, allowing users to understand and validate AI decisions.