Summary of Explaining Explaining, by Sergei Nirenburg et al.
Explaining Explaining
by Sergei Nirenburg, Marjorie McShane, Kenneth W. Goodman, Sanjay Oruganti
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the lack of explainability in machine learning-based AI systems, which are critical to high-stakes decision-making. The authors argue that redefining explanation as a machine-learning-centric approach is insufficient and propose a hybrid approach combining knowledge-based infrastructure with machine learning data. This new framework aims to develop cognitive agents that can assist humans in making decisions, taking responsibility for their actions. A demonstration system showcasing the explanatory potential of these agents illustrates this concept. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems are getting smarter, but they don’t explain why they make certain decisions. To make AI more trustworthy, researchers need a new approach. This paper suggests combining two ways AI works: one based on human knowledge and the other using machines to learn from data. By doing so, we can create AI assistants that help humans make good decisions, where humans are in charge. The authors show an example of how this could work by simulating a team of robots working together. |
Keywords
» Artificial intelligence » Machine learning
