Summary of Data Science Principles For Interpretable and Explainable Ai, by Kris Sankaran
Data Science Principles for Interpretable and Explainable AI
by Kris Sankaran
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Machine learning educators can now grasp the significance of interpretable and interactive machine learning, which aims to make complex models more transparent and controllable. The review synthesizes key principles from the growing literature in this field, introducing precise vocabulary for discussing interpretability. It explores connections to classical statistical and design principles, such as parsimony and gulfs of interaction, before illustrating basic explainability techniques like learned embeddings, integrated gradients, and concept bottlenecks with a simple case study. The review also discusses criteria for objectively evaluating interpretability approaches and underscores the importance of considering audience goals when designing interactive data-driven systems. Finally, it outlines open challenges and discusses the potential role of data science in addressing them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence is used more often than ever before, which brings both benefits and risks. Complex models can be deployed without understanding their impact. To solve this problem, interpretable and interactive machine learning aims to make these models more transparent and controllable. This means people can understand how the model works and make changes if needed. The review shows how to explain complex models in simple terms. It also talks about what makes a good explanation and why it’s important to think about who will be using the system. Overall, this is an important topic that affects many areas of life. |
Keywords
» Artificial intelligence » Machine learning
