[visionlist] [xAI4Biometrics Workshop @WACV2021] Firm Deadline: November, 5

[afilipaseq]

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xAI4Biometrics Workshop 2021 CALL FOR PAPERS
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xAI4Biometrics Workshop | WACV 2021 Workshop on Explainable & Interpretable
Artificial Intelligence for Biometrics |
*WACV 2021 J**anuary 05-09, 2021 * |  *Remote Event***

The WACV 2021 Workshop on Explainable & Interpretable Artificial
Intelligence for Biometrics (xAI4Biometrics Workshop 2021) 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 with keynote talks by:

   - *Cynthia Rudin**, *Duke University, USA
   - *Peter Eisert*, Humboldt University Berlin & Fraunhofer Institute for
   Telecommunications - HHI Berlin, Germany.



 xAI4Biometrics Workshop 2021 is organized by *INESC TEC*, Porto, Portugal
 For more information please visit http://vcmi.inesctec.pt/xai4biometrics


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 IMPORTANT DATES
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*Submission Deadline (**FIRM DEADLINE**):* *November 05, 2020*
*Acceptance Notification:* *November 20, 2020*
*Camera-ready & Registration: **November 30, 2020*
*Conference:* *January 05-09, 2021* / *Workshop Date: **TBD*



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 TOPICS OF INTEREST
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This Call for Papers 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 proof-of-concepts and
   demonstrators of how to integrate explainable AI into real-world work-flows
   and industrial processes.



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CONTACT
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Ana Filipa Sequeira, PhD
Assistant Researcher
INESC TEC, Porto, Portugal
ana.f.sequeira at inesctec.pt


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*Jaime S. Cardoso*, INESC TEC and University of Porto, Portugal.

*Matt Fredrikson*, Carnegie Mellon University, USA.

*Cynthia Rudin**, *Duke University, USA.
(xAI4Biometrics Workshop 2021 GENERAL CHAIRS)
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