[visionlist] ExFMA-2026 CfP - Explainability and Fairness in Multimedia Analysis - Deadline 20 April 2026

[Chiara Galdi]

/Apologies for multiple posting. Please feel free to forward to whom may 
be interested./


      [ExFMA-2026] Explainability and Fairness in Multimedia Analysis

https://cbmi2026.sciencesconf.org/resource/page/id/18#exfma

Recent advances in machine learning, and in particular deep learning, 
have led to remarkable performance gains in multimedia analysis tasks. 
However, it has also raised questions about the reliability, 
explicability, and fairness of their predictions for decision-making 
(e.g., the black box problem of the deep models and the risk of biased 
outcomes). This lack of transparency and potential unfairness raises 
many ethical and political concerns that prevent wider adoption of this 
potentially highly beneficial technology, especially when such systems 
are deployed in high-stakes or socially sensitive domains.

Most multimedia applications, such as person detection/tracking, face 
recognition, or lifelog analysis, involve sensitive personal 
information. This raises both legal issues, such as data protection and 
regulations in the ongoing European AI regulation, as well as ethical 
concerns related to discrimination, demographic bias, and potential 
misuse of these technologies.

These challenges are particularly acute in multimedia applications, 
where models operate on high-dimensional, multimodal data, and where 
predictions frequently rely on subtle semantic cues that are difficult 
to interpret even for human experts. Biases may emerge from data 
imbalance, annotation practices, model design, or deployment contexts, 
and may disproportionately affect certain individuals or communities. It 
is therefore crucial not only to understand how predictions correlate 
with information perception and expert decision-making but also whether 
they are equitable across groups and aligned with societal values. The 
objective of eXplainable AI (XAI) and Fair AI is to improve 
transparency, mitigate bias, and foster meaningful human

understanding of AI systems.

This special session focuses on methods and applications for explainable 
and fair multimedia analysis, with an emphasis on explanations that are 
faithful to the underlying models, meaningful to end users, actionable 
for domain experts, and supportive of bias detection and mitigation. The 
goal is to bring together researchers and practitioners working on 
theoretical, methodological, and applied aspects of explainability, 
fairness, evaluation, and interaction in multimedia AI systems.

Topics of interest include (but are not limited to):

  * Analysis of the influencing factors relevant to the final decision
    as an essential step to understand and improve the underlying
    processes involved.
  * Methods for bias detection, fairness assessment, and mitigation in
    multimedia dataset and models.
  * Fairness-aware learning strategies for multimedia analysis.
  * Information visualization for models or their predictions.
  * Visual analytics and Interactive applications for XAI.
  * Performance evaluation metrics and protocols for explainability.
  *   Performance evaluation metrics and protocols for fairness.
  * Sample-centric and dataset-centric explanations, including subgroup
    analyses
  * Attention mechanisms for XAI.
  * XAI-based pruning.
  * XAI for multimedia systems supporting domain experts (e.g.,
    healthcare, security, cultural heritage).
  * Open challenges from industry or existing and emerging regulatory
    frameworks.
  * Industrial use cases and deployment challenges.

The special session aims to collect high-quality scientific 
contributions that advance the state of the art in explainable and fair 
multimedia analysis, and to foster interdisciplinary discussion on how 
transparency, fairness, and accountability can be jointly addressed in 
multimedia AI systems. By integrating explainability and fairness, the 
session seeks to promote trustworthy AI technologies that enhance 
societal benefit while minimizing risks of bias, discrimination, and 
unintended harm.

*Important dates :*

Paper deadline: 20 APRIL  2026

Notification: 22 MAY  2026

Camera-ready: 15 JUNE 2026

Paper submission  :Author Guidelines 
<https://cbmi2026.sciencesconf.org/resource/page/id/12>

Please indicate in the comments that this paper is for SS *ExFMA-2026*

*SS chairs*

  * Chiara Galdi, EURECOM, Sophia Antipolis, France.
  * Romain Bourqui, Université of Bordeaux
  * Martin Winter, JOANNEUM RESEARCH - DIGITAL, Graz, Austria.
  * Romain Giot, Université of Bordeaux

-- 
Cordiali saluti / Bien cordialement / Kind regards,

Chiara

--
Chiara GALDI, PhD
Assistant Professor
Dept. of Digital Security
EURECOM Campus SophiaTech

450 Route des Chappes
06410 Biot Sophia Antipolis
FRANCE

galdi at eurecom.fr
Phone : +33 (0)4 93.00.81.67
Fax   : +33 (0)4 93.00.82.00
http://www.eurecom.fr/~galdi
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