Explainable Artificial Intelligence : Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part III / edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.

Format:

Book

Author/Creator:

Longo, Luca.

Contributor:

Lapuschkin, Sebastian.

Seifert, Christin.

Series:

Communications in Computer and Information Science, 1865-0937 ; 2155

Language:

English

Subjects (All):

Artificial intelligence.

Natural language processing (Computer science).

Application software.

Computer networks.

Artificial Intelligence.

Natural Language Processing (NLP).

Computer and Information Systems Applications.

Computer Communication Networks.

Local Subjects:

Artificial Intelligence.

Natural Language Processing (NLP).

Computer and Information Systems Applications.

Computer Communication Networks.

Physical Description:

1 online resource (471 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-63800-X

OCLC:

1446452217