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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 751–775 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
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