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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.
As the demand for more explainable machine learning models with interpretable predictions rises, so does the need for methods that can help to achieve these goals. This survey will focus on providing an extensive and in-depth identification, analysis, and comparison of machine learning interpretability methods.
28 de feb. de 2023 · Interpretability and explainability are essential principles of machine learning model and method design and development for medicine, economics, law, and natural sciences applications. Over the last 30 years, many techniques motivated by these properties have been developed.
17 de ago. de 2023 · Explainability in machine learning means that you can explain what happens in your model from input to output. It makes models transparent and solves the black box problem. Explainable AI (XAI) is the more formal way to describe this and applies to all artificial intelligence.
22 de sept. de 2022 · Explainable machine learning in materials science. Xiaoting Zhong, Brian Gallagher, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski & T. Yong-Jin Han. npj Computational Materials 8, Article...
1 de jul. de 2021 · In the very least, explainability can facilitate the understanding of various aspects of a model, leading to insights that can be utilized by various stakeholders, such as (cf. Figure 1 ): • Data scientists can be benefited when debugging a model or when looking for ways to improve performance.
1 de ene. de 2023 · Explainable AI (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. In recent years, various techniques have been proposed to explain and understand ML models, which have been previously widely considered black boxes (e.g., deep neural networks), and verify their predictions.
