| Vol-2640 urn:nbn:de:0074-2640-6 Copyright © 2020 for the individual papers by the papers' authors. Copyright © 2020 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 2020
Artificial Intelligence Safety 2020

### Proceedings of the Workshop on Artificial Intelligence Safety 2020
co-located with the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020)

### Yokohama, Japan, January, 2021.

Edited by

### Huáscar Espinoza *
John McDermid **
Xiaowei Huang ***
Mauricio Castillo-Effen ****
Xin Cynthia Chen *****
José Hernández-Orallo ******
Seán Ó hÉigeartaigh *******
Richard Mallah ********

* Commissariat à l´Energie Atomique, France
** University of York, United Kingdom
*** University of Liverpool, United Kingdom
**** Lockheed Martin, USA
***** University of Hong Kong, China
****** Universitat Politècnica de València, Spain
******* University of Cambridge, United Kingdom
******** Future of Life Institute, USA

## Table of Contents

- Preface

### Session 1: Adversarial Machine Learning

- Understanding the One Pixel Attack: Propagation Maps and Locality Analysis
  Danilo Vasconcellos Vargas, Jiawei Su
- Error-Silenced Quantization: Bridging Robustness and Compactness
  Zhicong Tang, Yinpeng Dong, Hang Su
- Evolving Robust Neural Architectures to Defend from Adversarial Attacks
  Shashank Kotyan, Danilo Vasconcellos Vargas

### Session 2: AI Safety Landscape

- Safety of Artificial Intelligence: A Collaborative Model
  John McDermid, Yan Jia

### Session 3: Safe and Value-Aligned Learning in Decision Making

- Choice Set Misspeciﬁcation in Reward Inference
  Rachel Freedman, Rohin Shah, Anca Dragan
- Safety Augmentation in Decision Trees
  Sumanta Dey, Pallab Dasgupta, Briti Gangopadhyay
- Aligning with Heterogenous Preferences for Kidney Exchange
  Rachel Freedman

### Session 4: DNN Testing and Runtime Monitoring

- Is Uncertainty Quantification in Deep Learning Sufficient for Out-of-Distribution Detection?
  Adrian Schwaiger, Poulami Sinhamahapatra, Jens Gansloser, Karsten Roscher
- DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning
  Samet Demir, Hasan Ferit Eniser, Alper Sen
- A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications
  Fabio Arnez, Huascar Espinoza, Ansgar Radermacher, François Terrier
- Increasing the Trustworthiness of Deep Neural Networks via Accuracy Monitoring
  Zhihui Shao, Jianyi Yang, Shaolei Ren

### Poster Papers

- Robustness as Inherent Property of Datapoints (short paper)
  Catalin-Andrei Ilie, Marius Popescu, Alin Stefanescu
- Towards Safe and Reliable Robot Task Planning (short paper)
  Snehasis Banerje
- Extracting Money from Causal Decision Theorists
  Caspar Oesterheld, Vincent Conitzer
- Ethically Compliant Planning in Moral Autonomous Systems
  Justin Svegliato, Samer Nashed, Shlomo Zilberstein
- Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers
  Andrey Morozov, Emil Valiev, Michael Beyer, Kai Ding, Lydia Gauerhof, Christoph Schorn

2020-07-15: submitted by Xin Cynthia Chen, metadata incl. bibliographic data published under Creative Commons CC0
2020-07-16: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|