👉 Explain Engineering, or Explainable AI (XAI), is a field focused on developing methods and techniques to make artificial intelligence systems more transparent, understandable, and interpretable. It aims to bridge the gap between complex AI models, often referred to as "black boxes," and human comprehension by providing clear explanations for their decision-making processes. This involves creating algorithms that can articulate why and how they arrive at specific conclusions, enabling users to trust and validate AI outputs. Explainable Engineering in AI is crucial for applications requiring accountability, such as healthcare and finance, where understanding the reasoning behind AI decisions can be vital for ethical and effective use. By enhancing transparency, Explainable Engineering helps in debugging models, improving their robustness, and ensuring compliance with regulatory standards.
