Explainable Artificial Intelligence

Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part IV
2024
ISBN: 978-3-031-63802-2
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 2156, 466 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 727 g

Reihe: Communications in Computer and Information Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Longo, Luca

Seifert, Christin

Lapuschkin, Sebastian

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

.- 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.