Explainable AI refers to techniques that make machine learning models more understandable to humans. As AI systems become increasingly complex, with neural networks containing millions of parameters, it can be difficult to comprehend why they make certain predictions. Explainable AI aims to shed light on these "black box" models by developing methods to analyze feature importance, visualize data flows, and generate text and visual explanations that articulate the reasoning behind AI decisions. By enhancing transparency and accountability, explainable AI fosters greater trust in AI systems. From inspecting self-driving car image classifiers, to explaining risk assessments in finance, explainable AI unlocks the opacity around machine learning models so that businesses can operate AI responsibly and users can understand the technology permeating their lives.

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