What are the main challenges in explainable AI?

What are the main challenges in explainable AI?

The main challenges in explainable AI include model complexity, lack of standardization, and the trade-off between accuracy and interpretability.

Can you elaborate on model complexity?

Sure! Model complexity refers to how intricate a model is, which can make it difficult to understand how decisions are made.

What about the lack of standardization?

Lack of standardization means there are no universally accepted methods for explaining AI models, leading to inconsistencies.

How does the trade-off between accuracy and interpretability work?

In many cases, more accurate models, like deep learning, are harder to interpret, while simpler models are easier to understand but may sacrifice accuracy.

What are some potential solutions to these challenges?

Potential solutions include developing better visualization tools, creating standardized metrics for explainability, and using hybrid models that balance complexity and interpretability.