Explainable artificial intelligence
204 papers with code • 0 benchmarks • 8 datasets
XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
Benchmarks
These leaderboards are used to track progress in Explainable artificial intelligence
Libraries
Use these libraries to find Explainable artificial intelligence models and implementations
BDD-X
e-SNLI-VE
OpenXAI
e-ViL
AnthroProtect
ExBAN
EUCA dataset
Latest papers with no code
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models
The field of eXplainable artificial intelligence (XAI) has produced a plethora of methods (e. g., saliency-maps) to gain insight into artificial intelligence (AI) models, and has exploded with the rise of deep learning (DL).
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
In the former branch, we train the CNN with a CAW layer inserted to perform skin lesion diagnosis.
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
The study introduces an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer using the CBIS-DDSM dataset.
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs.
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation
Deep learning models are widely applied in the signal processing community, yet their inner working procedure is often treated as a black box.
SHIELD: A regularization technique for eXplainable Artificial Intelligence
As Artificial Intelligence systems become integral across domains, the demand for explainability grows.
Explainable AI Integrated Feature Engineering for Wildfire Prediction
In our research, we conducted a thorough assessment of various machine learning algorithms for both classification and regression tasks relevant to predicting wildfires.
Energy Model-based Accurate Shapley Value Estimation for Interpretable Deep Learning Predictive Modelling
As a favorable tool for explainable artificial intelligence (XAI), Shapley value has been widely used to interpret deep learning based predictive models.
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators
This is the first work on automatic analysis of legal texts combining NLP and ML along with Explainable Artificial Intelligence techniques to automatically make the models' decisions understandable to end users.
Transparent and Clinically Interpretable AI for Lung Cancer Detection in Chest X-Rays
The rapidly advancing field of Explainable Artificial Intelligence (XAI) aims to tackle the issue of trust regarding the use of complex black-box deep learning models in real-world applications.
