| Vol-3856 urn:nbn:de:0074-3856-1 Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 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 2024
IJCAI 2024 Workshop on Artificial Intelligence Safety

### Proceedings of the IJCAI 2024 Workshop on Artificial Intelligence Safety (AISafety 2024)
co-located with the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)

### Jeju, Korea, August 4, 2024.

Edited by

### Gabriel Pedroza *
Xiaowei Huang **
Xin Cynthia Chen ***
Fabio Arnez ****
Huascar Espinoza *****
José Hernández-Orallo ******
Mauricio Castillo-Effen *******
Richard Mallah ********
John McDermid *********
Andreas Theodorou **********

* ANSYS, France
** University of Liverpool, UK
*** ETH Zurich, Switzerland
**** CEA LIST, France
***** Chips JU, Belgium
****** Universitat Politècnica de València, Spain
******* Lockheed Martin, USA
******** Future of Life Institute, USA
********* University of York, UK
********** Universitat Politècnica de Catalunya, Spain

## Table of Contents

- Preface
  Summary: There were 11 papers submitted for peer-review to this workshop. Out of these, 8 papers were accepted for this volume, all 8 as regular papers.

### Session 1: AI Safety Assessment: Validation Methods and Techniques

- ReLESS: A Framework for Assessing Safety in Deep Learning Systems
  Anita Raja, Nan Jia, Raffi Khatchadourian
- Enhancing Autonomous Vehicle Safety through N-version Machine Learning Systems
  Qiang Wen, Júlio Mendonça, Fumio Machida, Marcus Völp

### Session 2: AI Robustness: Adversarial Learning and Security/Privacy

- Hyper-parameter Tuning for Adversarially Robust Models
  Pedro Mendes, Paolo Romano, David Garlan
- Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC
  Ke Lin, Yasir Glani, Ping Luo

### Session 3: Safety of Generative AI: OoD and Human vs Machine Generative Detection

- Detecting out-of-distribution text using topological features of transformer-based language models
  Andres Pollano, Anupam Chaudhuri, Anj Simmons
- The Impact of Prompts on Zero-Shot Detection of AI-Generated Text
  Kouichi Sakurai, Kaito Taguchi, Yujie Gu

### Session 4: AI Robustness: Resilience to Noise and Soft Errors

- Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models
  Qutub Syed, Michael Paulitsch, Karthik Pattabiraman, Korbinian Hagn, Fabian Oboril, Cornelius Buerkle, Kay-Ulrich Scholl, Gereon Hinz, Alois Knoll
- Neural Vicinal Risk Minimization: Noise-robust Distillation for Noisy Labels
  Hyounguk Shon, Seunghee Koh, Yunho Jeon, Junmo Kim

2024-10-21: submitted by Xin Cynthia Chen, metadata incl. bibliographic data published under Creative Commons CC0
2024-12-02: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|