Explainable Machine Learning (XAI) is crucial as AI systems increasingly impact critical decisions in areas like healthcare and finance. These systems often operate as "black boxes" with little transparency, which is problematic when decisions have significant consequences. XAI helps demystify these processes by making AI decisions understandable and justifiable.

There are two main approaches to XAI: model-agnostic methods that apply across different models, and model-specific methods that are tailored to particular types of models, such as visualizing decision trees or neural network attention maps. These tools not only increase trust among users by making AI systems more transparent but also ensure compliance with regulations such as the GDPR, which mandates a "right to explanation."

Despite its advantages, implementing XAI is not without challenges. There's often a trade-off between a model's interpretability and its accuracy. Additionally, explanations must be both accurate and user-friendly, which can be especially challenging for non-technical stakeholders.

For instance, in healthcare, AI that can explain its diagnostic decisions can improve collaboration between AI systems and medical professionals. In banking, explainable AI models can make credit scoring processes transparent, aiding both customer service and compliance with regulatory standards.

As AI continues to evolve, the integration of explainable frameworks is crucial. This shift not only enhances the performance and accountability of AI systems but also fosters an ethical approach to technology deployment, encouraging wider acceptance and trust in AI applications.