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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

Showing 191200 of 971 papers

TitleStatusHype
Explainability of Machine Learning Models under Missing DataCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Evaluating saliency methods on artificial data with different background typesCode0
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in ExplanationsCode0
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust PerspectiveCode0
Conditional Feature Importance with Generative Modeling Using Adversarial Random ForestsCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Explainability in Music Recommender SystemsCode0
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