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Produktbild: Explainable Artificial Intelligence
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Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III

Aus der Reihe Communications in Computer and Information Science
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76,99 € UVP 87,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2024

Abbildungen

XVII, 456 p. 130 illus., 103 illus. in color.

Herausgeber

Luca Longo + weitere

Verlag

Springer

Seitenzahl

456

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

715 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63799-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2024

Abbildungen

XVII, 456 p. 130 illus., 103 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

456

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

715 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63799-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Explainable Artificial Intelligence

  • .- 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.