Summary of Abstaining Machine Learning — Philosophical Considerations, by Daniela Schuster
Abstaining Machine Learning – Philosophical Considerations
by Daniela Schuster
First submitted to arxiv on: 1 Sep 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 This paper explores the intersection between machine learning (ML) and philosophy in the context of neutral behavior. It focuses on a specific class of ML systems called abstaining machine learning systems, which have not been studied from a philosophical perspective before. The authors introduce various types of these systems and examine how they relate to the concept of suspended judgment in epistemology. They also discuss the autonomy and explainability of the abstaining responses generated by these systems. The study argues that one type of abstaining system is more suitable than others, as it aligns better with our criteria for suspended judgment and can autonomously generate explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning systems can behave in a neutral way. It’s like when you’re trying to make a decision, but you don’t have enough information yet, so you just say “I don’t know”. The authors are interested in a special kind of ML system that does this, called an abstaining system. They look at different types of these systems and how they relate to our understanding of being unsure or skeptical. They also think about whether these systems can explain themselves when they make neutral decisions. |
Keywords
» Artificial intelligence » Machine learning
