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SafeAI@AAAI 2023: Washington, DC, USA
- Gabriel Pedroza, Xiaowei Huang, Xin Cynthia Chen, Andreas Theodorou, José Hernández-Orallo, Mauricio Castillo-Effen, Richard Mallah, John A. McDermid:
Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023) co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023), Washington DC, USA, February 13-14, 2023. CEUR Workshop Proceedings 3381, CEUR-WS.org 2023
Session 1: AI/ML Learning, Explainability, Accuracy and Policy Alignment
- Peter Barnett, Rachel Freedman, Justin Svegliato, Stuart Russell:
Active Reward Learning from Multiple Teachers. - Saaduddin Mahmud, Sandhya Saisubramanian, Shlomo Zilberstein:
REVEALE: Reward Verification and Learning Using Explanations. - Maxime Fuccellaro, Laurent Simon, Akka Zemmari:
A Robust Drift Detection Algorithm with High Accuracy and Low False Positives Rate.
Session 2: Short Presentations - Safety Assessment of AI-enabled systems
- Salah Ghamizi, Maxime Cordy, Mike Papadakis, Yves Le Traon:
On Evaluating Adversarial Robustness of Chest X-ray Classification. - Fateh Kaakai, Paul-Marie Raffi:
Towards Multi-timescale Online Monitoring of AI Models. - Chenyang Yang, Rachel A. Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu:
Capabilities for Better ML Engineering.
Session 3: AI/ML for Safety Critical Applications: Assurance Cases and Datasets
- Chiara Picardi, Richard Hawkins, Colin Paterson, Ibrahim Habli:
Transfer Assurance for Machine Learning in Autonomous Systems. - Václav Divis, Tobias Schuster, Marek Hrúz:
Domain-centric ADAS Datasets. - Maryam Bagheri, Josephine Lamp, Xugui Zhou, Lu Feng, Homa Alemzadeh:
Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems.
Session 4 - Short Presentations: ML/DL Robustness: GAM and Attack Detection
- Weimin Zhao, Sanaa A. Alwidian, Qusay H. Mahmoud:
Evaluation of GAN Architectures for Adversarial Robustness of Convolution Classifier. - Khondoker Murad Hossain, Tim Oates:
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks.
Session 5 - AI Safety Assessment: Failure-Cause Analysis, Assurance, Verification
- Nikiforos Pittaras, Sean McGregor:
A Taxonomic System for Failure Cause Analysis of Open Source AI Incidents. - Felippe Schmoeller Roza, Simon Hadwiger, Ingo Thon, Karsten Roscher:
Towards Safety Assurance of Uncertainty-Aware Reinforcement Learning Agents. - Valency Oscar Colaco, Simin Nadjm-Tehrani:
Formal Verification of Tree Ensembles against Real-World Composite Geometric Perturbations.
Session 6: AI Robustness: Adversarial and Attacks Learning
- Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn:
Critically Assessing the State of the Art in CPU-based Local Robustness Verification. - Soumyadeep Pal, Ren Wang, Yuguang Yao, Sijia Liu:
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. - Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan A. Goldhahn:
Less is More: Data Pruning for Faster Adversarial Training. - Teddy Ferdinan, Jan Kocon:
Personalized Models Resistant to Malicious Attacks for Human-centered Trusted AI.
Session 7: AI Robustness: Deep Reinforcement Learning
- Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ricardo Luna Gutierrez, Antonio Guillen, Avisek Naug:
Robustness with Black-Box Adversarial Attack using Reinforcement Learning. - Stephen Casper, Dylan Hadfield-Menell, Gabriel Kreiman:
White-Box Adversarial Policies in Deep Reinforcement Learning. - Chen Chen, Haibo Hong, Mande Xie, Jun Shao, Tao Xiang:
Bab: A novel algorithm for training clean model based on poisoned data. - Sumanta Dey, Pallab Dasgupta, Soumyajit Dey:
Safe Reinforcement Learning through Phasic Safety-Oriented Policy Optimization.
Session 8 - Short Presentations: OoD Detection and Uncertainty for ML/DL Safety
- Fabio Arnez, Ansgar Radermacher, François Terrier:
Out-of-Distribution Detection Using Deep Neural Network Latent Space Uncertainty. - Tian Tan, Carlos Huertas, Qi Zhao:
Efficient and Effective Uncertainty Quantification in Gradient Boosting via Cyclical Gradient MCMC. - Dirk Eilers, Simon Burton, Felippe Schmoeller da Roza, Karsten Roscher:
Safety Assurance with Ensemble-based Uncertainty Estimation and overlapping alternative Predictions in Reinforcement Learning.
Session 9 - Short Presentations: Methods and Techniques for AI/ML Safety Assessment
- Juliette Mattioli, Henri Sohier, Agnès Delaborde, Gabriel Pedroza, Kahina Amokrane-Ferka, Afef Awadid, Zakaria Chihani, Souhaiel Khalfaoui:
Towards a holistic approach for AI trustworthiness assessment based upon aids for multi-criteria aggregation. - Alberto Huertas Celdrán, Jan Kreischer
, Melike Demirci, Joel Leupp, Pedro Miguel Sánchez Sánchez, Muriel Figueredo Franco, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller:
A Framework Quantifying Trustworthiness of Supervised Machine and Deep Learning Models. - Axel Brando, Isabel Serra, Enrico Mezzetti, Francisco J. Cazorla, Jaume Abella:
Standardizing the Probabilistic Sources of Uncertainty for the sake of Safety Deep Learning.
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