Explainable Artificial Intelligence

Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III

AvLuca Longo,Sebastian Lapuschkin

Häftad, Engelska, 2024

Del 2155 i serien Communications in Computer and Information Science

901 kr

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Beskrivning

This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on:Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI.Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI.Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI.Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence.

Produktinformation

Utgivningsdatum:2024-07-10
Mått:155 x 235 x 26 mm
Vikt:715 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:456
Förlag:Springer International Publishing AG
ISBN:9783031637995

Utforska kategorier

Hårdvara inom Data och IT
Tillämpad datateknik inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

.- Counterfactual explanations and causality for eXplainable AI..- Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems..- Human-in-the-loop Personalized Counterfactual Recourse..- COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images..- Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence..- CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests..- Causality-Aware Local Interpretable Model-Agnostic Explanations..- Evaluating the Faithfulness of Causality in Saliency-based Explanations of Deep Learning Models for Temporal Colour Constancy..- CAGE: Causality-Aware Shapley Value for Global Explanations..- Fairness, trust, privacy, security, accountability and actionability in eXplainable AI..- Exploring the Reliability of SHAP Values in Reinforcement Learning..- Categorical Foundation of Explainable AI: A Unifying Theory..- Investigating Calibrated Classification Scores through the Lens of Interpretability..- XentricAI: A Gesture Sensing Calibration Approach through Explainable and User-Centric AI..- Toward Understanding the Disagreement Problem in Neural Network Feature Attribution..- ConformaSight: Conformal Prediction-Based Global and Model-Agnostic Explainability Framework..- Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability..- Blockchain for Ethical & Transparent Generative AI Utilization by Banking & Finance Lawyers..- Multi-modal Machine learning model for Interpretable Mobile Malware Classification..- Explainable Fraud Detection with Deep Symbolic Classification..- Better Luck Next Time: About Robust Recourse in Binary Allocation Problems..- Towards Non-Adversarial Algorithmic Recourse..- Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring..- XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users.