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Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part III /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explainable artificial intelligence/ edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
其他題名:	second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.
其他題名:	xAI 2024
其他作者:	Longo, Luca.
團體作者:	World Conference on Explainable Artificial Intelligence
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xvii, 456 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-63800-8
ISBN:	9783031638008

Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part III /
Explainable artificial intelligencesecond World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.Part III /[electronic resource] :xAI 2024edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert. - Cham :Springer Nature Switzerland :2024. - xvii, 456 p. :ill. (some col.), digital ;24 cm. - Communications in computer and information science,21551865-0937 ;. - Communications in computer and information science ;2155..

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.

ISBN: 9783031638008

Standard No.: 10.1007/978-3-031-63800-8doiSubjects--Topical Terms:

606815
Artificial intelligence
--Congresses.

LC Class. No.: Q334

Dewey Class. No.: 006.3

Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part III /
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