Summary of Towards Integrating Personal Knowledge Into Test-time Predictions, by Isaac Lage et al.
Towards Integrating Personal Knowledge into Test-Time Predictions
by Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez
First submitted to arxiv on: 12 Jun 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 A novel machine learning challenge is introduced, where models lack personal knowledge about the individuals they are predicting outcomes for. For instance, a model predicting psychiatric outcomes may not know about a patient’s social support system, which can vary greatly between patients. To address this issue, researchers propose the concept of human feature integration, allowing non-experts to incorporate crucial personal-knowledge into ML predictions. This is characterized through user stories and comparisons to existing approaches, formally described as a foundation for future technical solutions, and demonstrated in a proof-of-concept study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can make decisions based on big data, but they often miss important information that humans know about the people being predicted for. For example, a model predicting mental health outcomes might not know about a person’s social support system. This paper talks about how to fix this problem by letting non-experts add personal-knowledge into ML predictions. They give examples and compare it to existing methods, then show a simple way to do this in a realistic setting. |
Keywords
» Artificial intelligence » Machine learning
