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Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases.
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.
Dec 5, 2023 · What is Explainable AI? Explainable artificial intelligence (XAI) refers to a collection of procedures and techniques that enable machine learning algorithms to produce output and results that are understandable and reliable for human users.
Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of...
Nov 1, 2023 · Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years.
Jun 23, 2023 · Explainable AI is a set of techniques, principles and processes that aim to help AI developers and users alike better understand AI models, both in terms of their algorithms and the outputs generated by them.
Nov 28, 2023 · Explainable AI (XAI) techniques provide the means to try to unravel the mysteries of AI decision-making, helping end users easily understand and interpret model predictions. This post explores...
Jul 12, 2021 · Abstract: Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions.
Jan 17, 2022 · Explainable artificial intelligence (XAI) is a powerful tool in answering critical How? and Why? questions about AI systems and can be used to address rising ethical and legal concerns. As a result, AI researchers have identified XAI as a necessary feature of trustworthy AI, and explainability has experienced a recent surge in attention.
Nov 16, 2023 · We’re creating tools to help debug AI, where systems can explain what they’re doing. This includes training highly optimized, directly interpretable models, as well as explanations of black-box models and visualizations of neural network information flows.
