SafeAI 2019
AAAI Workshop on Artificial Intelligence Safety

Table of Contents

• Preface
  

Frontmatter

• Author Index
  
• Keyword Index
  
• Table of Contents

Invited Talk

• Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function)
  Peter Eckersley

Session 1: Safe Planning and Operation of Autonomous Systems

• Minimizing the Negative Side Effects of Planning with Reduced Models
  Sandhya Saisubramanian , Shlomo Zilberstein
• Robust Motion Planning and Safety Benchmarking in Human Workspaces
  Shih-Yun Lo , Shani Alkoby , Peter Stone
• Enter the Matrix: Safely Interruptible Autonomous Systems via Virtualization
  Mark Riedl , Brent Harrison

Session 2: New Paradigms in AI and AGI Safety

• Towards Robust End-to-End Alignment
  Lê Nguyên Hoang
• Integrative Biological Simulation, Neuropsychology, and AI Safety
  Gopal Sarma , Adam Safron , Nick Hay

Session 3: Safety in Automated Driving

• How Many Operational Design Domains, Objects, and Events?
  Philip Koopman , Frank Fratrik
• Monitoring Safety of Autonomous Vehicles with Crash Prediction Networks
  Saasha Nair , Sina Shafaei , Stefan Kugele , Mohd Hafeez Osman , Alois Knoll

Session 4: Safety-Related AI Requirements and Characteristics

• Requirements Assurance in Machine Learning
  Alec Banks , Rob Ashmore
• Surveying Safety-relevant AI Characteristics
  Jose Hernandez-Orallo , Fernando Martínez-Plumed , Shahar Avin , Sean O Heigeartaigh

Session 5: Adversarial Machine Learning

• Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
  Bryant Chen , Wilka Carvalho , Nathalie Baracaldo , Heiko Ludwig , Benjamin Edwards , Taesung Lee , Ian Molloy , Biplav Srivastava
• DPATCH: An Adversarial Patch Attack on Object Detectors
  Xin Liu , Huanrui Yang , Ziwei Liu , Linghao Song , Yiran Chen , Hai Li
• Attacks on Machine Learning: Lurking Danger for Accountability
  Katja Auernhammer , Ramin Tavakoli Kolagari , Markus Zoppelt

Short Poster Papers

• Towards International Standards for Evaluating Machine Learning
  Frank Rudzicz , P Alison Paprica , Marta Janczarski
• Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety
  Kacper Sokol , Peter Flach
• Safe Temporal Planning for Urban Driving
  Bence Cserna , William Doyle , Tianyi Gu , Wheeler Ruml
• Linking Artificial Intelligence Principles
  Yi Zeng , Enmeng Lu , Cunqing Huangfu
• Emergence of Addictive Behaviors in Reinforcement Learning Agents
  Vahid Behzadan , Roman V. Yampolskiy , Arslan Munir
• Temporally Extended Metrics for Markov Decision Processes
  Philip Amortila , Marc G. Bellemare , Prakash Panangaden , Doina Precup
• AutoMPC: Efficient Multi-Party Computation for Secure and Privacy-Preserving Cooperative Control of Connected Autonomous Vehicles
  Tao Li , Lei Lin , Siyuan Gong
• Security-preserving Support Vector Machine with Fully Homomorphic Encryption
  Saerom Park , Jaewook Lee , Jung Hee Cheon , Juhee Lee , Jaeyun Kim , Junyoung Byun
• Bamboo: Ball-Shape Data Augmentation Against Adversarial Attacks from All Directions
  Huanrui Yang , Jingchi Zhang , Hsin-Pai Cheng , Wenhan Wang , Yiran Chen , Hai Li