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ORCIDxAI 2023: Lisbon, Portugal
- Luca Longo
:
Explainable Artificial Intelligence - First World Conference, xAI 2023, Lisbon, Portugal, July 26-28, 2023, Proceedings, Part II. Communications in Computer and Information Science 1902, Springer 2023, ISBN 978-3-031-44066-3
Surveys, Benchmarks, Visual Representations and Applications for xAI
- Igor Cherepanov, David Sessler, Alex Ulmer, Hendrik Lücke-Tieke, Jörn Kohlhammer:
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features. 3-23 - Antonin Poché, Lucas Hervier, Mohamed Chafik Bakkay:
Natural Example-Based Explainability: A Survey. 24-47 - Blerta Abazi Chaushi, Besnik Selimi, Agron Chaushi, Marika Apostolova:
Explainable Artificial Intelligence in Education: A Comprehensive Review. 48-71 - Xiaowei Liu, Kevin McAreavey, Weiru Liu:
Contrastive Visual Explanations for Reinforcement Learning via Counterfactual Rewards. 72-87 - Mohamed Karim Belaid, Richard Bornemann, Maximilian Rabus, Ralf Krestel, Eyke Hüllermeier:
Compare-xAI: Toward Unifying Functional Testing Methods for Post-hoc XAI Algorithms into a Multi-dimensional Benchmark. 88-109 - Laura State, Hadrien Salat, Stefania Rubrichi, Zbigniew Smoreda:
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal. 110-125 - Daniel Gierse, Felix Neubürger, Thomas Kopinski:
A Novel Architecture for Robust Explainable AI Approaches in Critical Object Detection Scenarios Based on Bayesian Neural Networks. 126-147
xAI for Decision-Making and Human-AI Collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks
- Luca Corbucci, Riccardo Guidotti, Anna Monreale:
Explaining Black-Boxes in Federated Learning. 151-163 - Erwin Walraven, Ajaya Adhikari, Cor J. Veenman:
PERFEX: Classifier Performance Explanations for Trustworthy AI Systems. 164-180 - Charles Wan, Rodrigo Belo, Leid Zejnilovic, Susana Lavado:
The Duet of Representations and How Explanations Exacerbate It. 181-197 - Thales Bertaglia, Stefan Huber, Catalina Goanta, Gerasimos Spanakis, Adriana Iamnitchi:
Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media. 198-213 - Arthur Picard, Yazan Mualla, Franck Gechter, Stéphane Galland:
Human-Computer Interaction and Explainability: Intersection and Terminology. 214-236 - Javier Jiménez Raboso, Antonio Manjavacas, Alejandro Campoy-Nieves, Miguel Molina-Solana, Juan Gómez-Romero:
Explaining Deep Reinforcement Learning-Based Methods for Control of Building HVAC Systems. 237-255 - Martina Cinquini, Fosca Giannotti, Riccardo Guidotti, Andrea Mattei:
Handling Missing Values in Local Post-hoc Explainability. 256-278 - Christophe Labreuche, Roman Bresson:
Necessary and Sufficient Explanations of Multi-Criteria Decision Aiding Models, with and Without Interacting Criteria. 279-302 - Eli J. Laird, Ayesh Madushanka, Elfi Kraka, Corey Clark:
XInsight: Revealing Model Insights for GNNs with Flow-Based Explanations. 303-320 - Hongbo Bo, Yiwen Wu, Zinuo You, Ryan McConville, Jun Hong, Weiru Liu:
What Will Make Misinformation Spread: An XAI Perspective. 321-337 - Jonas Teufel, Luca Torresi, Patrick Reiser, Pascal Friederich:
MEGAN: Multi-explanation Graph Attention Network. 338-360 - Jonas Teufel, Luca Torresi, Pascal Friederich:
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies. 361-381 - Claudio Borile, Alan Perotti, André Panisson:
Evaluating Link Prediction Explanations for Graph Neural Networks. 382-401
Actionable eXplainable AI, Semantics and Explainability, and Explanations for Advice-Giving Systems
- Danilo Cavaliere, Mariacristina Gallo, Claudio Stanzione:
Propaganda Detection Robustness Through Adversarial Attacks Driven by eXplainable AI. 405-419 - Leonardo Arrighi, Sylvio Barbon Junior, Felice Andrea Pellegrino, Michele Simonato, Marco Zullich:
Explainable Automated Anomaly Recognition in Failure Analysis: is Deep Learning Doing it Correctly? 420-432 - Deepan Chakravarthi Padmanabhan, Paul G. Plöger, Octavio Arriaga, Matias Valdenegro-Toro:
DExT: Detector Explanation Toolkit. 433-456 - Md Shajalal, Sebastian Denef, Md. Rezaul Karim, Alexander Boden, Gunnar Stevens:
Unveiling Black-Boxes: Explainable Deep Learning Models for Patent Classification. 457-474 - Francesco Dibitonto, Fabio Garcea, André Panisson, Alan Perotti, Lia Morra:
HOLMES: HOLonym-MEronym Based Semantic Inspection for Convolutional Image Classifiers. 475-498 - Georgii Mikriukov, Gesina Schwalbe, Christian Hellert, Korinna Bade:
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability. 499-524 - Alan Perotti, Simone Bertolotto, Eliana Pastor, André Panisson:
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers. 525-548 - Kirill Bykov, Laura Kopf, Marina M.-C. Höhne:
Finding Spurious Correlations with Function-Semantic Contrast Analysis. 549-572 - Zhangyi Wu, Tim Draws, Federico Cau, Francesco Barile, Alisa Rieger, Nava Tintarev:
Explaining Search Result Stances to Opinionated People. 573-596 - Roan Schellingerhout, Francesco Barile, Nava Tintarev:
A Co-design Study for Multi-stakeholder Job Recommender System Explanations. 597-620 - Clara Punzi, Aleksandra Maslennikova, Gizem Gezici, Roberto Pellungrini, Fosca Giannotti:
Explaining Socio-Demographic and Behavioral Patterns of Vaccination Against the Swine Flu (H1N1) Pandemic. 621-635 - Jacqueline Höllig, Aniek F. Markus, Jef de Slegte, Prachi Bagave:
Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility. 636-659
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