The explainable artificial intelligence foundation (XAIF) has launched! This organization aims to provide AI/ML developers, practitioners, users, leaders and others a central location for the latest XAI best practices and resources.
Explainable AI (XAI) refers to the ability of an artificial intelligence (AI) system or model to provide clear and understandable explanations for its actions or decisions. In other words, XAI is about making AI transparent and interpretable to humans.
In many cases, AI models are trained using complex algorithms and large datasets, which can make it difficult for humans to understand how the model arrived at a particular decision. This lack of transparency can create problems in areas such as healthcare, finance, and law, where decisions made by AI systems can have significant real-world consequences.
XAI seeks to address this issue by developing AI models that can provide clear and understandable explanations for their decisions. This can help increase trust in AI systems and ensure that they are used appropriately and ethically.
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