Here are gathered tools that can be used to make out work more efficient through the whole model lifecycle. The unified grammar beyond DrWhy.AI universe is described in the Explanatory Model Analysis: Explore, Explain and Examine Predictive Models book. Lifecycle for Predictive Models The DrWhy ...
XAI is the method of making AI decision-making processes understandable and accessible to human users.
Frameworks, architectures, algorithms, tools for post-hoc/ante-hoc explainability Theoretical approaches of explainability and transparent AI Human intelligence vs. Artificial Intelligence (HCI — KDD) Interactive machine learning with human(s)-in-the-loop (crowd intelligence) Explanation User Interfa...
especially under regulations like GDPR. XAI, by its nature, seeks to reveal the inner workings of an AI model, and this transparency can inadvertently expose sensitive personal information embedded in the training data [1-3]. For instance, if an AI model is trained on medical records to predi...
Explores the essential tools and strategies that make AI interpretable to build compliant, trustworthy systems.
An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple te...
This fusion aims to not only make AI systems transparent but also intuitively understandable and usable for a broad spectrum of users, ranging from AI experts to laypersons. Our framework emphasizes the development of explainable AI tools that are not only technically sound but also empathetic to ...
On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values[J]. Geoscience Frontiers, 2024, 15(4): 101800. DOI: 10.1016/j.gsf.2024.101800 Citation: Nan Wang, Hongyan Zhang, Ashok Dahal, Weiming Cheng, Min Zhao, Luigi Lombardo. On the use of...
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI ) pyss3.readthedocs.io Topics nlp machine-learning natural-language-processing text-mining data-mining text-classification machine-learning-algorithms artificial-inte...
Introducing concepts SeXAI: Introducing Concepts into Black Boxes for Explainable Artificial Intelligence Paper Tensorflow 1.4 Additive explainers Learning simplified functions to understand Paper BIN Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier’s Decision Arxiv Te...