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Explainable artificial intelligence

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.

Papers

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Showing 751–800 of 971 papers

TitleStatusHype
Knowledge-based XAI through CBR: There is more to explanations than models can tell—0
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning—0
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies—0
Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text—0
Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes—0
Evaluating saliency methods on artificial data with different background typesCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Model-agnostic explainable artificial intelligence for object detection in image dataCode0
Class-Dependent Perturbation Effects in Evaluating Time Series AttributionsCode0
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structureCode0
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive BiasCode0
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course DesignCode0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Discrete Subgraph Sampling for Interpretable Graph based Visual Question AnsweringCode0
Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement LearningCode0
Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive LearningCode0
Detecting mental disorder on social media: a ChatGPT-augmented explainable approachCode0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in ExplanationsCode0
Explainability in Music Recommender SystemsCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Explainability of Machine Learning Models under Missing DataCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Generative and Explainable AI for High-Dimensional Channel EstimationCode0
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CTCode0
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust PerspectiveCode0
ShapG: new feature importance method based on the Shapley valueCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Glocal Explanations of Expected Goal Models in SoccerCode0
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial IntelligenceCode0
Bounded logit attention: Learning to explain image classifiersCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Natural Language Counterfactual Explanations for Graphs Using Large Language ModelsCode0
cito: An R package for training neural networks using torchCode0
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and PrognosisCode0
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationCode0
VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language ModelsCode0
Characterizing the contribution of dependent features in XAI methodsCode0
Delivering Inflated ExplanationsCode0
Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition EstimationCode0
Ensemble of Counterfactual ExplainersCode0
TrustyAI Explainability ToolkitCode0
Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric riverCode0
NN2Rules: Extracting Rule List from Neural NetworksCode0
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