• Methods to interpret the biometric models to validate their decisions as well as to improve the models and to detect possible vulnerabilities;
• Quantitative methods to objectively assess and compare different explanations of the automatic decisions;
• Methods and metrics to study/evaluate the quality of explanations obtained by post-model approaches and improve the explanations;
• Methods to generate model-agnostic  explanations;
• Transparency and fairness in AI algorithms avoiding bias;
• Methods that use post-model explanations to improve the models’ training;
• Methods to achieve/design inherently interpretable algorithms (rule-based, case-based reasoning, regularization methods);
• Study on causal learning, causal discovery, causal reasoning, causal explanations, and causal inference;
• Natural Language generation for explanatory models;
• Methods for adversarial attacks detection, explanation and defense (“How can we interpret adversarial examples?”);
• Theoretical approaches of explainability (“What makes a good explanation?”);
• Applications of all the above including proofs-of-concept and demonstrators of how to integrate explainable AI into real-world workflows and industrial processes.

ORGANIZING COMMITTEES

• Jaime S. Cardoso, INESC TEC and University of Porto, Portugal
• Ana F. Sequeira, INESC TEC, Porto, Portugal
• Arun Ross, Michigan State University, USA
• Peter Eisert, Humboldt University & Fraunhofer HHI
• Cynthia Rudin, Duke University, USA

• Christoph Busch, NTNU & Hochschule Darmstadt
• Tiago de Freitas Pereira, IDIAP Research Institute, Switzerland
• Wilson Silva, INESC TEC and University of Porto, Portugal