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

• Autore: Longo Luca
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (480 pages)
• Disciplina: 006.3
• Soggetto topico: 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
• Altri autori: LapuschkinSebastian SeifertChristin
• Nota di contenuto: -- 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Explainable Artificial Intelligence
• ISBN: 3-031-63803-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910872189603321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science, . 1865-0937 ; ; 2156