Explainable and Interpretable Artificial Intelligence for Biometrics Call for Papers
*******************************************************************
xAI4Biometrics Workshop @ WACV 2022 :: Call for Papers
*******************************************************************
The WACV 2022 2nd Workshop on Explainable & Interpretable Artificial
Intelligence for Biometrics (xAI4Biometrics Workshop 2022) intends to
promote research on Explainable & Interpretable-AI to facilitate the
implementation of AI/ML in the biometrics domain, and specifically to
help facilitate transparency and trust.
This workshop will include two keynote talks by:
Walter J. Scheirer, Notre Dame University, USA
Speaker TBA
The xAI4Biometrics Workshop 2022 is organized by INESC TEC, Porto,
Portugal. For more information please visit
http://vcmi.inesctec.pt/xai4biometrics
IMPORTANT DATES
Abstract submission (mandatory): October 04, 2021
Full Paper Submission Deadline: October 11, 2021
Acceptance Notification: November 15, 2021
Camera-ready & Registration: November 19, 2021
Conference: January 04-08, 2022 | Workshop Date: January 04, 2022
TOPICS OF INTEREST
The xAI4Biometrics welcomes works that focus on biometrics and promote
the development of:
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
GENERAL CHAIRS
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
PROGRAMME CHAIRS
Christoph Busch, NTNU & Hochschule Darmstadt
Tiago de Freitas Pereira, IDIAP Research Institute, Switzerland
Wilson Silva, INESC TEC and University of Porto, Portugal
CONTACT
Ana Filipa Sequeira, PhD (ana.f.sequeira@inesctec.pt)
Assistant Researcher
INESC TEC, Porto, Portugal