What is the difference between explainable AI and black box AI?

What is the difference between explainable AI and black box AI?

Explainable AI refers to models that provide clear insights into their decision-making processes, while black box AI lacks transparency, making it hard to understand how decisions are made.

Can you give me examples of each?

Sure! An example of explainable AI is a decision tree, where you can see the path taken for a decision. A black box example is a deep learning model, like a neural network, where the internal workings are not easily interpretable.

Why is explainability important in AI?

Explainability is crucial for trust, accountability, and compliance, especially in sensitive areas like healthcare and finance, where understanding decisions can impact lives.

Are there any drawbacks to explainable AI?

Yes, explainable AI can sometimes be less accurate than complex black box models, and it may require more computational resources to interpret.