Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part IV /
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 a...
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| その他の著者: | , , |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
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Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| 版: | 1st ed. 2024. |
| シリーズ: | Communications in Computer and Information Science,
2156 |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-3-031-63803-9 |
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| 245 | 1 | 0 | |a Explainable Artificial Intelligence |h [electronic resource] : |b Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part IV / |c edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert. |
| 250 | |a 1st ed. 2024. | ||
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| 505 | 0 | |a -- 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 700 | 1 | |a Longo, Luca. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Lapuschkin, Sebastian. |e editor. |0 (orcid)0000-0002-0762-7258 |1 https://orcid.org/0000-0002-0762-7258 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Seifert, Christin. |e editor. |0 (orcid)0000-0002-6776-3868 |1 https://orcid.org/0000-0002-6776-3868 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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