What is interpretability in AI?

Interpretability in AI refers to the ability to understand and explain how artificial intelligence systems, especially complex models like neural networks, make their decisions. It helps researchers, developers, and end-users to trust AI systems by making their inner workings more transparent. Interpretability is crucial in sensitive fields such as healthcare and finance, where decisions need to be justified and understood. Techniques for interpretability include feature importance, visualization, and model simplification. Improving interpretability can lead to safer, fairer, and more accountable AI systems.