Summary of Towards Compositional Interpretability For Xai, by Sean Tull et al.
Towards Compositional Interpretability for XAI
by Sean Tull, Robin Lorenz, Stephen Clark, Ilyas Khan, Bob Coecke
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Category Theory (math.CT)
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 This paper tackles the challenge of interpretability in artificial intelligence (AI), specifically addressing the lack of transparency in machine learning models. By developing eXplainable AI (XAI) techniques, researchers aim to improve accountability and trustworthiness in high-stakes domains like finance, law, and healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is a type of computer programming that can think for itself. Right now, most AI is based on “black box” machines that we don’t fully understand how they work. This is a problem because it’s hard to know why they make certain decisions. Scientists are working on a way to explain how these machines work, which is called eXplainable AI (XAI). This is important because we need to be able to trust the decisions made by these machines in areas like money, law, and healthcare. |
Keywords
» Artificial intelligence » Machine learning
