Summary of Minimising Changes to Audit When Updating Decision Trees, by Anj Simmons et al.
Minimising changes to audit when updating decision trees
by Anj Simmons, Scott Barnett, Anupam Chaudhuri, Sankhya Singh, Shangeetha Sivasothy
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Medium Difficulty Summary: This paper proposes an algorithm for updating decision trees on new training data while minimizing the number of changes required to audit by humans. The approach, which incorporates the number of changes into the objective function, is shown to sit between final accuracy and number of changes in a sweet spot when compared to existing methods. By leveraging a greedy strategy, the algorithm efficiently updates the tree while considering the human auditing aspect, making it more interpretable and accountable. This work has implications for real-world applications where model transparency and explainability are crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Imagine you have a decision-making tool that makes predictions based on rules. But what if new data comes in and the tool needs to be updated? The problem is that humans need to review these updates to make sure they’re fair and accurate. This paper suggests a way to update the tool while keeping the number of changes minimal, so it’s easier for humans to understand what changed and why. By doing this, the algorithm helps maintain transparency and accountability in decision-making processes. |
Keywords
» Artificial intelligence » Objective function
