Explainable Artificial Intelligence : Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part IV / 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 ; 2156

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 (480 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:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-63803-4

OCLC:

1446451897