Summary of Combining Ai Control Systems and Human Decision Support Via Robustness and Criticality, by Walt Woods et al.
Combining AI Control Systems and Human Decision Support via Robustness and Criticality
by Walt Woods, Alexander Grushin, Simon Khan, Alvaro Velasquez
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary AI-enabled capabilities are reaching maturity for real-world deployment, but their decisions may not always be correct or safe. To address these concerns, AI control systems can support human decisions in safe situations and rely on humans for critical ones. Our paper extends a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks like MuZero. We propose multiple improvements to the base agent architecture and demonstrate two applications: intelligent decision tools and enhancing training/learning frameworks. AE helps users make correct decisions by highlighting contextual factors that would change AI-recommended decisions. Additionally, AEs help identify robustness against adversarial tampering. Strategically similar autoencoders (SSAs) aid users in understanding salient factors considered by the AI system. In a training framework, this technology improves AI’s decisions and explanations through human interaction. Finally, we tie this combined system to our prior art on statistically verified analyses of critical decision points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems are getting better at making decisions, but they’re not always right or safe. To fix this, we suggest using AI control systems that work with humans in important situations and rely on humans for tricky choices. Our new method helps explain why AI makes certain decisions by highlighting the factors that would change its mind. This can help us make good choices and avoid mistakes. We also show how AI explanations can help identify when AI is making a mistake or being tricked. Finally, we demonstrate how this combined system can improve AI’s decision-making through human interaction. |
Keywords
* Artificial intelligence * Reinforcement learning
