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

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

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

    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 modelsí

    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

    Applications of all the above including proofs-of-concept and
    demonstrators of how to integrate explainable AI into real-world
    workflows and industrial processes.



        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

Ana Filipa Sequeira, PhD (ana.f.sequeira@inesctec.pt)
Assistant Researcher
INESC TEC, Porto, Portugal