Explainable Artificial Intelligence

Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I
2024
ISBN: 978-3-031-63786-5
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 2153, 494 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 768 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 Angewandte Informatik
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

.- Intrinsically interpretable XAI and concept-based global explainability.
.- Seeking Interpretability and Explainability in Binary Activated Neural Networks.
.- Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges.
.- Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.
.- Revisiting FunnyBirds evaluation framework for prototypical parts networks.
.- CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models.
.- Unveiling the Anatomy of Adversarial Attacks: Concept-based XAI Dissection of CNNs.
.- AutoCL: AutoML for Concept Learning.
.- Locally Testing Model Detections for Semantic Global Concepts.
.- Knowledge graphs for empirical concept retrieval.
.- Global Concept Explanations for Graphs by Contrastive Learning.
.- Generative explainable AI and verifiability.
.- Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation.
.- Generative Inpainting for Shapley-Value-Based Anomaly Explanation.
.- Challenges and Opportunities in Text Generation Explainability.
.- NoNE Found: Explaining the Output of Sequence-to-Sequence Models when No Named Entity is Recognized.
.- Notion, metrics, evaluation and benchmarking for XAI.
.- Benchmarking Trust: A Metric for Trustworthy Machine Learning.
.- Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI.
.- Conditional Calibrated Explanations: Finding a Path between Bias and Uncertainty.
.- Meta-evaluating stability measures: MAX-Sensitivity & AVG-Senstivity.
.- Xpression: A unifying metric to evaluate Explainability and Compression of AI models.
.- Evaluating Neighbor Explainability for Graph Neural Networks.
.- A Fresh Look at Sanity Checks for Saliency Maps.
.- Explainability, Quantified: Benchmarking XAI techniques.
.- BEExAI: Benchmark to Evaluate Explainable AI.
.- Associative Interpretability of Hidden Semantics with Contrastiveness Operators in Face Classification tasks.