Bytes of Knowledge 09: What is Explainable AI (XAI)

What is Explainable AI (XAI)
Explainable AI (XAI) focuses on making AI systems' decision-making processes more understandable and transparent. Unlike traditional "black-box" models, XAI aims to provide clear explanations for AI outcomes, fostering trust, accountability, and ethical practices.
XAI is crucial in fields like healthcare, finance, and law enforcement, where AI decisions directly impact lives. It helps ensure compliance with regulations such as GDPR, which require transparency in automated decision-making. However, challenges like the complexity of AI models, trade-offs between performance and interpretability, and scalability remain.
Approaches to XAI include model-specific methods (like decision trees), post-hoc techniques (like LIME and SHAP), and visualization tools. The future of XAI includes hybrid models that balance accuracy with interpretability, stronger regulations, and more education on AI transparency.
Ultimately, XAI is essential for responsible AI deployment, making AI more trustworthy and ethical for widespread use.