| Vol-3505 urn:nbn:de:0074-3505-0 Copyright © 2023 for the individual papers by the papers' authors. Copyright © 2023 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# AISafety-SafeRL 2023
Artificial Intelligence Safety and Safe Reinforcement Learning 2023

### Proceedings of the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023)
co-located with the 32nd International Joint Conference on Artificial Intelligence(IJCAI2023)

### Macau, China, August 21-22, 2023.

Edited by

### Gabriel Pedroza *
Xiaowei Huang **
Xin Cynthia Chen ***
Andreas Theodorou ****
Huascar Espinoza *****
Nikolaos Matragkas ******
José Hernández-Orallo *******
Mauricio Castillo-Effen ********
Richard Mallah *********
John McDermid **********
David Bossens ***********
Bettina Koenighofer ************
Sebastian Tschiatschek *************
Anqi Liu **************

* ANSYS, France
** University of Liverpool, United Kingdom
*** ETH Zurich, Switzerland
**** Umeå University, Sweden
***** KDT JU, Belgium
****** CEA LIST, France
******* Universitat Politècnica de València, Spain
******** Lockheed Martin, USA
********* Future of Life Institute, USA
********** University of York, United Kingdom
*********** University of Southampton, United Kingdom
************ TU Graz, Austria
************* University of Vienna, Austria
************** Johns Hopkins University, USA

## Table of Contents

- Preface
  Summary: There were 19 papers submitted for peer-review to this workshop. Out of these, 10 papers were accepted for this volume.

### Session 1: Robustness of AI via OoD and Unknown-Unknowns Dectection

- Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution
  Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann
- Unsupervised Unknown Unknown Detection in Active Learning
  Prajit T. Rajendran, Huascar Espinoza, Agnes Delaborde, Chokri Mraidha

### Session 2: AI Robustness, Adversarial Attacks and Reinforcemnt Learning

- PerCBA: Persistent Clean-label Backdoor Attacks on Semi-Supervised Graph Node Classification
  Xiao Yang, Gaolei Li, Chaofeng Zhang, Meng Han, Wu Yang
- Distribution-restrained Softmax Loss for the Model Robustness
  Chen Li, Hao Wang, Jinzhe Jiang, Xin Zhang, Yaqian Zhao, Weifeng Gong
- Fear Field: Adaptive constraints for safe environment transitions in Shielded Reinforcement Learning
  Haritz Odriozola-Olalde, Nestor Arana-Arexolaleiba, Maider Zamalloa, Jon Perez-Cerrolaza, Jokin Arozamena-Rodríguez

### Session 3: AI Governance and Policy/Value Alignment

- An open source perspective on AI and alignment with the EU AI Act
  Diego Calanzone, Andrea Coppari, Riccardo Tedoldi, Giulia Olivato, Carlo Casonato

### Session 4: Safe RL

- Risk-sensitive Actor-free Policy via Convex Optimisation
  Ruoqi Zhang, Jens Sjölund

### Session 5: AI Trustworthiness, Explainability and Testing

- Empirical Optimal Risk to Quantify Model Trustworthiness for Failure Detection
  Shuang Ao, Stefan Rueger, Advaith Siddharthan
- Weight-based Semantic Testing Approach for Deep Neural Networks
  Amany Alshareef, Nicolas Berthier, Sven Schewe, Xiaowei Huang
- AI for Safety: How to use Explainable Machine Learning Approaches for Safety Analyses
  Iwo Kurzidem, Simon Burton, Philipp Schleiss

2023-09-27: submitted by Cynthia Chen, metadata incl. bibliographic data published under Creative Commons CC0
2023-10-14: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|