SafeAI 2019

SafeAI is the AAAI's Workshop on Artificial Intelligence Safety.

It was held in January 27, 2019, in Honolulu, Hawaii (USA) as part of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) - one of the world's leading AI conferencea.  SafeAI aims to explore new ideas on AI safety engineering, ethically aligned design, regulation and standards for AI-based systems. These regular workshops embed safety in the wider field, and provide a publication venue for high-quality AI safety research. 22 papers were presented, and can be read in full here.

The Best Paper Award was won by a team from IBM Research: 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

The other best paper candidates were: 

• Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function) - Peter Eckersley (an Invited Talk)
• Requirements Assurance in Machine Learning - Alec Banks, Rob Ashmore
• Robust Motion Planning and Safety Benchmarking in Human Workspaces - Shih-Yun Lo, Shani Alkoby, Peter Stone
• Surveying Safety-relevant AI Characteristics - Jose Hernandez-Orallo, Fernando Martínez-Plumed, Shahar Avin, Sean O Heigeartaigh

The Workshop also featured Keynotes:

• Dr. Sandeep Neema (DARPA), Assured Autonomy
• Prof. Francesca Rossi (IBM and University of Padova), Ethically Bounded AI

and Invited Talks:

• Dr. Ian Goodfellow (Google Brain), Adversarial Robustness for AI Safety
• Prof. Alessio R. Lomuscio (Imperial College London), Reachability Analysis for Neural Agent-Environment Systems

Read full proceedings

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