Explainable artificial intelligence Part 3
.- 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 se...
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| Weitere Verfasser: | , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
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| Schriftenreihe: | Communications in computer and information science
2155 |
| Schlagworte: |
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> Artificial intelligence
> COMPUTERS / Artificial Intelligence
> COMPUTERS / Data Processing / Speech & Audio Processing
> COMPUTERS / Enterprise Applications
> COMPUTERS / Networking / General
> Künstliche Intelligenz
> Natural language & machine translation
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| Zusammenfassung: | .- 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. 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xvii, 456 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031637995 978-3-031-63799-5 |