Overview

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.

Full Product Details

Author:   Luca Longo ,  Sebastian Lapuschkin ,  Christin Seifert
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   2156
ISBN:  

9783031638022

ISBN 10:   3031638026
Pages:   466
Publication Date:   10 July 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

.- Explainable AI in healthcare and computational neuroscience. .- SRFAMap: a method for mapping integrated gradients of a CNN trained with statistical radiomic features to medical image saliency maps. .- Transparently Predicting Therapy Compliance of Young Adults Following Ischemic Stroke. .- Precision medicine in student health: Insights from Tsetlin Machines into chronic pain and psychological distress. .- Evaluating Local Explainable AI Techniques for the Classification of Chest X-ray Images. .- Feature importance to explain multimodal prediction models. A clinical use case. .- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. .- Increasing Explainability in Time Series Classification by Functional Decomposition. .- Towards Evaluation of Explainable Artificial Intelligence in Streaming Data. .- Quantitative Evaluation of xAI Methods for Multivariate Time Series - A Case Study for a CNN-based MI Detection Model. .- Explainable AI for improved human-computer interaction and Software Engineering for explainability. .- Influenciae: A library for tracing the influence back to the data-points. .- Explainability Engineering Challenges: Connecting Explainability Levels to Run-time Explainability. .- On the Explainability of Financial Robo-advice Systems. .- Can I trust my anomaly detection system? A case study based on explainable AI.. .- Explanations considered harmful: The Impact of misleading Explanations on Accuracy in hybrid human-AI decision making. .- Human emotions in AI explanations. .- Study on the Helpfulness of Explainable Artificial Intelligence. .- Applications of explainable artificial intelligence. .- Pricing Risk: An XAI Analysis of Irish Car Insurance Premiums. .- Exploring the Role of Explainable AI in the Development and Qualification of Aircraft Quality Assurance Processes: A Case Study. .- Explainable Artificial Intelligence applied to Predictive Maintenance: Comparison of Post-hoc Explainability Techniques. .- A comparative analysis of SHAP, LIME, ANCHORS, and DICE for interpreting a dense neural network in Credit Card Fraud Detection. .- Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data. .- Ensuring Safe Social Navigation via Explainable Probabilistic and Conformal Safety Regions. .- Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments. .- AcME-AD: Accelerated Model Explanations for Anomaly Detection.

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