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 E ects of Planning with Reduced Models . . . . . . . . . . . . . . . . . . Sandhya Saisubramanian and Shlomo Zilberstein Robust Motion Planning and Safety Benchmarking in Human Workspaces . . . . . . . . . . . . . . . . 16 Shih-Yun Lo, Shani Alkoby and Peter Stone Enter the Matrix: Safely Interruptible Autonomous Systems via Virtualization . . . . . . . . . . . 25 Mark Riedl and Brent Harrison Session 2: New Paradigms in AI and AGI Safety Integrative Biological Simulation, Neuropsychology, and AI Safety . . . . . . . . . . . . . . . . . . . . . . . . 40 Gopal Sarma, Adam Safron and Nick Hay Session 3: Safety in Automated Driving How Many Operational Design Domains, Objects, and Events? . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Philip Koopman and Frank Fratrik Monitoring Safety of Autonomous Vehicles with Crash Prediction Networks . . . . . . . . . . . . . . 49 Saasha Nair, Sina Shafaei, Stefan Kugele, Mohd Hafeez Osman and Alois Knoll Session 4: Safety-Related AI Requirements and Characteristics Requirements Assurance in Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Alec Banks and Rob Ashmore Surveying Safety-relevant AI Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Jose Hernandez-Orallo, Fernando Mart nez-Plumed, Shahar Avin and Sean O Heigeartaigh Session 5: Adversarial Machine Learning Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering . . . . . . . . . . 66 Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy and Biplav Srivastava DPATCH: An Adversarial Patch Attack on Object Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Yiran Chen and Hai Li
9 Towards Robust End-to-End Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
L^e Nguy^en Hoang Short Poster Papers Towards international standards for evaluating machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Frank Rudzicz, P Alison Paprica and Marta Janczarski Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Kacper Sokol and Peter Flach Safe Temporal Planning for Urban Driving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Bence Cserna, William Doyle, Tianyi Gu and Wheeler Ruml Linking Arti cial Intelligence Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Yi Zeng, Enmeng Lu and Cunqing Huangfu