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xAI aims to discover the answers to life, the universe, and everything with its multimodal model Grok. Learn about Grok's features, latest updates, and career opportunities on xAI's blog.
Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production.
Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this.
XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning.
May 24, 2021 · Explainable AI (XAI) is a set of tools and techniques that help people understand the math inside AI models to provide greater transparency on decision-making. Learn about the benefits, challenges and methods of XAI, and see examples of how it is used in various industries.
Dec 5, 2023 · Learn what XAI is, why it is needed, and how it works. XAI is a process and a set of methods that help users understand the results and output of AI/ML algorithms.
Nov 28, 2023 · Learn how XAI techniques help users understand and interpret AI models, and how responsible AI principles ensure fairness, transparency, accountability, and privacy. Explore popular XAI frameworks and tools, and see how they fit into the big picture of trustworthy AI.
