Summary of On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios, by Stylianos Loukas Vasileiou et al.
On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios
by Stylianos Loukas Vasileiou, William Yeoh, Alessandro Previti, Tran Cao Son
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel framework for generating probabilistic monolithic explanations and model reconciling explanations is proposed, aiming to increase transparency and understanding in AI systems’ decisions. The framework integrates uncertainty using probabilistic logic to provide self-contained reasons for an explanandum. Model reconciling explanations account for the knowledge of the agent receiving the explanation, with a goal to minimize conflicts between the explanation and the probabilistic human model. Quantitative metrics such as explanatory gain and explanatory power assess the quality of these explanations. The approach is demonstrated on various benchmarks, showcasing its effectiveness and scalability in generating explanations under uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to explain why AI systems make certain decisions. Right now, it’s hard for humans to understand how AI works because AI uses uncertain information and probabilistic models. To fix this problem, the researchers developed two types of explanations: monolithic explanations that provide self-contained reasons and model reconciling explanations that take into account what the person receiving the explanation knows. They also came up with new metrics to measure how good these explanations are. By testing their approach on different datasets, they showed it works well even when there’s a lot of uncertainty. |
