Explainable AI
Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable to humans. It focuses on creating transparent, interpretable, and accountable AI systems by providing insights into how models make decisions or predictions. This is crucial for building trust, ensuring fairness, and meeting regulatory requirements in high-stakes applications.
Developers should learn Explainable AI when working on AI systems in domains like healthcare, finance, or autonomous vehicles, where understanding model decisions is critical for safety, ethics, and compliance. It helps debug models, identify biases, and communicate results to stakeholders, making it essential for responsible AI development and deployment in regulated industries.