CFP ESANN 2025 Special Session on Reliability, Safety and Robustness of AI applications
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Caroline König
Sep 15, 2025, 11:16:57 PM (2 days ago) Sep 15
to Machine Learning News
* Apologies for cross-posting*
Special Session on Reliability, Safety and Robustness of AI applications
ESANN 2026: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
22 to 24 April 2026, Bruges, Belgium
Special Session on Reliability, Safety and Robustness of AI applications
ESANN 2026: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
22 to 24 April 2026, Bruges, Belgium
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Reliability, safety, and robustness in AI models are critical for real-world applications. Machine learning models must be designed to operate reliably under practical conditions for real-world systems, including when those conditions differ from the training environment. This session will focus on recent advancements in applied AI for safety- critical applications, as well as methodological contributions that enhance reliability, support safe deployment, and enable robust testing of AI systems.
We invite submissions on (but not limited to) the following themes:
- Safety-Critical AI Applications: Case studies and risk assessment frameworks
- Robustness Under Distribution Shifts: Techniques addressing open-set recognition, out-of-distribution detection and domain adaptation.
- Adversarial Robustness and Stress Testing: Evaluating model behavior under unknown or challenging inputs.
- Reliability Testing and Evaluation Protocols: Systematic validation approaches for model trustworthiness.
- Human-in-the-Loop Safety: Integrating expert oversight in high-risk AI deployments.
- Explainability for Safety-Critical Decisions: Model transparency for accountability.
- Formal Verification of AI Models: Methods to prove stability, fairness, and scalable verification.
- Uncertainty Quantification and Calibration: Confidence-aware predictions to support safe decisions.
IMPORTANT DATES
Deadline for submissions: 19 November 2025
Notification of decisions: 23 January 2026
We are looking forward to seeing you in Bruges!
Reliability, safety, and robustness in AI models are critical for real-world applications. Machine learning models must be designed to operate reliably under practical conditions for real-world systems, including when those conditions differ from the training environment. This session will focus on recent advancements in applied AI for safety- critical applications, as well as methodological contributions that enhance reliability, support safe deployment, and enable robust testing of AI systems.
We invite submissions on (but not limited to) the following themes:
- Safety-Critical AI Applications: Case studies and risk assessment frameworks
- Robustness Under Distribution Shifts: Techniques addressing open-set recognition, out-of-distribution detection and domain adaptation.
- Adversarial Robustness and Stress Testing: Evaluating model behavior under unknown or challenging inputs.
- Reliability Testing and Evaluation Protocols: Systematic validation approaches for model trustworthiness.
- Human-in-the-Loop Safety: Integrating expert oversight in high-risk AI deployments.
- Explainability for Safety-Critical Decisions: Model transparency for accountability.
- Formal Verification of AI Models: Methods to prove stability, fairness, and scalable verification.
- Uncertainty Quantification and Calibration: Confidence-aware predictions to support safe decisions.
IMPORTANT DATES
Deadline for submissions: 19 November 2025
Notification of decisions: 23 January 2026
We are looking forward to seeing you in Bruges!
Organizing Committee: Caroline König, Cecilio Angulo, Pedro Jesús Copado (Universitat Politècnica de Catalunya, Spain), G. Kumar Venayagamoorthy (Clemson University, USA)
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