Explainable Artificial Intelligence
Springer International Publishing (Verlag)
9783031637865 (ISBN)
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.
.- 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.
| Erscheinungsdatum | 11.07.2024 |
|---|---|
| Reihe/Serie | Communications in Computer and Information Science |
| Zusatzinfo | XVII, 494 p. 143 illus., 137 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Ante-hoc approaches for interpretability • argumentative-based approaches for explanations • Artificial Intelligence • Auto-encoders & explainability of latent spaces • Case-based explanations for AI systems • causal inference & explanations • convolutional neural networks • decomposition of neural network-based models for XAI • explainability • Explainable Artificial Intelligence • Graph neural networks for explainability • Human rights for explanations in AI systems • interpretable machine learning • Interpretable representational learning • Interpreting & explaining Convolutional Neural Networks • Model-specific vs model-agnostic methods for XAI • natural language processing for explanations • Neural networks • Neuro-symbolic reasoning for XAI • reinforcement learning for enhancing XAI |
| ISBN-13 | 9783031637865 / 9783031637865 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
