Summary of Designing An Interpretable Interface For Contextual Bandits, by Andrew Maher et al.
Designing an Interpretable Interface for Contextual Bandits
by Andrew Maher, Matia Gobbo, Lancelot Lachartre, Subash Prabanantham, Rowan Swiers, Puli Liyanagama
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 tackles the interpretability challenge of personalized recommender systems based on contextual bandits. To address this issue, the authors design a new interface to explain the underlying behavior of these systems to non-expert operators. A key metric used is “value gain”, which measures the real-world impact of sub-components within a bandit through off-policy evaluation. The paper conducts a qualitative user study to evaluate the effectiveness of the proposed interface, demonstrating that it can empower non-experts to manage complex machine learning systems. This work highlights the importance of balancing technical rigor with accessible presentation when designing interfaces for non-expert users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make personalized recommenders easier to understand and use. Right now, experts who run these systems have trouble figuring out why they’re working or not working well. To fix this, the authors created a new tool that shows how different parts of the system are affecting its overall performance. They used something called “value gain” to measure how well each part is doing. The authors tested their tool with some users and found that it helps non-experts understand and control these systems better. |
Keywords
* Artificial intelligence * Machine learning
