Summary of Explaining Decisions in Ml Models: a Parameterized Complexity Analysis, by Sebastian Ordyniak et al.
Explaining Decisions in ML Models: a Parameterized Complexity Analysis
by Sebastian Ordyniak, Giacomo Paesani, Mateusz Rychlicki, Stefan Szeider
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Complexity (cs.CC)
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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 investigates the parameterized complexity of explanation problems in various machine learning models with transparent internal mechanisms. The study focuses on two types of explanation problems: abductive and contrastive, both in local and global variants. The analysis encompasses diverse ML models, including Decision Trees, Random Forests, and Boolean Circuits, each offering unique explanatory challenges. By providing a foundational understanding of the complexities of generating explanations for these models, this research fills a significant gap in explainable AI (XAI). This work contributes to the broader discourse on the necessity of transparency and accountability in AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models can be understood by explaining what they do. It focuses on models that are transparent inside, not just black boxes. The study explores two ways of explaining: abductive (figuring out why something happened) and contrastive (explaining why one thing is different from another). The analysis includes many types of ML models, each with its own challenges for explanation. By understanding how to explain these models, this research helps make AI more transparent and accountable. |
Keywords
» Artificial intelligence » Discourse » Machine learning
