 |
 Modelling GDPR-Compliant Explanations for Trustworthy AIInternational Conference on Electronic Government and the Information Systems Perspective (ICEGISP), 2020 |
 Improving HRI through robot architecture transparencyInternational Journal of Social Robotics (JSR), 2021 |
 Trading Complexity for Sparsity in Random Forest ExplanationsAAAI Conference on Artificial Intelligence (AAAI), 2021 |
 Logic Explained NetworksInternational Workshop on Neural-Symbolic Learning and Reasoning (NeSy), 2021 |
 Post-hoc Interpretability for Neural NLP: A SurveyACM Computing Surveys (CSUR), 2021 |
 VBridge: Connecting the Dots Between Features and Data to Explain
Healthcare ModelsIEEE Transactions on Visualization and Computer Graphics (TVCG), 2021 |
 Discovering User-Interpretable Capabilities of Black-Box Planning AgentsInternational Conference on Principles of Knowledge Representation and Reasoning (KR), 2021 |
 The Who in XAI: How AI Background Shapes Perceptions of AI ExplanationsInternational Conference on Human Factors in Computing Systems (CHI), 2021 |
 Improving Visualization Interpretation Using CounterfactualsIEEE Transactions on Visualization and Computer Graphics (TVCG), 2021 |
 Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI
InteractionsAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021 |
 A Framework and Benchmarking Study for Counterfactual Generating Methods
on Tabular DataApplied Sciences (AS), 2021 |
 Exploring the Efficacy of Automatically Generated Counterfactuals for
Sentiment AnalysisAnnual Meeting of the Association for Computational Linguistics (ACL), 2021 |
 Contrastive Counterfactual Visual Explanations With OverdeterminationMachine-mediated learning (ML), 2021 |
 Explanatory Pluralism in Explainable AIInternational Cross-Domain Conference on Machine Learning and Knowledge Extraction (CD-MAKE), 2021 |
