Safety and Robustness

Summary

Safety and Robustness in AI alignment research encompasses a wide range of critical challenges aimed at ensuring AI systems perform reliably, securely, and ethically across various scenarios. This field addresses issues such as adversarial attacks, uncertainty quantification, out-of-distribution detection, and bias correction. Key areas of focus include developing robust neural network architectures, implementing safe exploration techniques in reinforcement learning, mitigating reward tampering, and creating containment strategies for advanced AI systems. Researchers are also working on improving fairness in AI decision-making, verifying neural network properties, and enhancing model performance under distribution shifts. The field emphasizes the importance of creating AI systems that not only maintain their capabilities but also remain aligned with their intended objectives when faced with novel or challenging situations. As AI systems become more powerful and widely deployed, addressing these safety and robustness concerns is crucial for ensuring the responsible development and implementation of AI technologies that benefit humanity while minimizing potential risks.

Sub-topics

• Adversarial Machine Learning
• Architecture Search
• Uncertainty Quantification
• Safe Exploration
• Reward Tampering
• ML Safety Challenges
• Containment
• Bias Correction
• Out-of-Distribution Detection
• Safety Envelopes
• Multi-Agent Safety
• Toxicity Mitigation
• AI-Generated Code Security
• Incident Databases
• Robust Representations
• Fairness
• Verification of Neural Networks
• Network Pruning
• Objective Robustness
• Data Processing for Robustness
• Dynamic Adversarial Training
• Debugging Neural Networks
• Adversarial Threats
• Collaborative Agents Robustness
• Evolutionary Algorithms for Atari
• Atari Safety
• Distribution Shift Robustness
• Generalization in Safety-Critical RL
• Psychopathological Approaches
• Human Motion Prediction