| Vol-2808 urn:nbn:de:0074-2808-8 Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 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).

# SafeAI 2021
Artificial Intelligence Safety 2021

### Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021)
co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021)

### Virtual, February 8, 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: Dynamic Safety and Anomaly Assessment

- Feature Space Singularity for Out-of-Distribution Detection
  Haiwen Huang, Zhihan Li, Lulu Wang, Sishuo Chen, Xinyu Zhou, Bin Dong
- An Evaluation of “Crash Prediction Networks” (CPN) for Autonomous Driving Scenarios in CARLA Simulator
  Saasha Nair, Sina Shafaei, Daniel Auge, Alois Knoll
- From Black-box to White-box: Examining Confidence Calibration under different Conditions
  Franziska Schwaiger, Maximilian Henne, Fabian Küppers, Felippe Schmoeller Roza, Karsten Roscher, Anselm Haselhoff

### Session 2: Safety Considerations for the Assurance of AI-based Systems

- The Utility of Neural Network Test Coverage Measures
  Rob Ashmore, Alec Banks
- Safety Properties of Inductive Logic Programming
  Gavin Leech, Nandi Schoots, Joar Skalse
- A Hybrid-AI Approach for Competence Assessment of Automated Driving functions
  Jan-Pieter Paardekooper, Mauro Comi, Corrado Grappiolo, Ron Snijders, Willeke van Vught, Rutger Beekelaar

### Session 3: Adversarial Machine Learning and Trustworthiness

- Adversarial Robustness for Face Recognition: How to Introduce Ensemble Diversity among Feature Extractors?
  Takuma Amada, Kazuya Kakizaki, Seng Pei Liew, Toshinori Araki, Joseph Keshet, Jun Furukawa
- Multi-Modal Generative Adversarial Networks Make Realistic and Diverse but Untrustworthy Predictions When Applied to Ill-posed Problems
  John Hyatt, Michael Lee
- DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation
  Javier Hernandez-Ortega, Ruben Tolosana, Julian Fierrez, Aythami Morales

### Session 4: Safe Autonomous Agents

- What criminal and civil law tells us about Safe RL techniques to generate law-abiding behaviour
  Hal Ashton
- Performance of Bounded-Rational Agents With the Ability to Self-Modify
  Jakub Tětek, Marek Sklenka, Tomáš Gavenčiak
- Deep CPT-RL: Imparting Human-Like Risk Sensitivity to Artificial Agents
  Jared Markowitz, Marie Chau, I-Jeng Wang
- Challenges for Using Impact Regularizers to Avoid Negative Side Effects
  David Lindner, Kyle Matoba, Alexander Meulemans

### Poster Papers

- Towards an Ontological Framework for Environmental Survey Hazard Analysis of Autonomous Systems
  Christopher Harper, Praminda Caleb-Solly
- Overestimation Learning with Guarantees
  Adrien Gauffriau, François Malgouyres, Mélanie Ducoffe
- On the Use of Available Testing Methods for Verification & Validation of AI-based Software and Systems
  Franz Wotawa
- Runtime Decision Making Under Uncertainty in Autonomous Vehicles
  Vibhu Gautam, Youcef Gheraibia, Rob Alexander, Richard Hawkins
- Negative Side Effects and AI Agent Indicators: Experiments in SafeLife
  John Burden, Jose Hernandez-Orallo, Sean O'Heigeartaigh
- Time for AI (Ethics) Maturity Model Is Now
  Ville Vakkuri, Marianna Jantunen, Erika Halme, Kai-Kristian Kemell, Anh Nguyen-Duc, Tommi Mikkonen, Pekka Abrahamsson
- AI-Blueprint for Deep Neural Networks
  Ernest Wozniak, Henrik Putzer, Carmen Carlan
- Neural Criticality: Validation of Convolutional Neural Networks
  Vaclav Divis, Marek Hruz
- Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data
  Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, Olivier Elshocht
- Correct-by-Construction Multi-Label Classification Networks
  Eleonora Giunchiglia, Thomas Lukasiewicz
- Classification Confidence Scores with Point-wise Guarantees
  Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner

2021-01-22: submitted by Xin Cynthia Chen, metadata incl. bibliographic data published under Creative Commons CC0
2021-02-07: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|