Summary of Improving the Validity and Practical Usefulness Of Ai/ml Evaluations Using An Estimands Framework, by Olivier Binette and Jerome P. Reiter
Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework
by Olivier Binette, Jerome P. Reiter
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Methodology (stat.ME)
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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 The proposed framework aims to bridge the gap between benchmark performances and real-world applications by introducing an estimands framework from international clinical trials guidelines. This systematic approach emphasizes well-defined estimation targets, providing a structure for inference and reporting in evaluations. The framework is demonstrated on common evaluation methodologies like cross-validation, clustering, and LLM benchmarking, which can lead to incorrect model rankings (rank reversals) even with large performance differences. By uncovering underlying issues, causes, and potential solutions, the estimands framework can improve the validity of evaluations and help decision-makers interpret results more effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models are usually tested on special datasets called benchmarks. This helps researchers develop new methods, but it’s not always clear how well these models will work in real life. The problem is that benchmark performances might not match what happens in real-world applications – a kind of mismatch. To fix this, we suggest using an approach from medical research trials to evaluate AI models more accurately. This framework helps define exactly what you’re trying to measure and report on. We show how this works with common evaluation methods like cross-validation and clustering, which can lead to mistakes even when the differences are big. Our goal is to make evaluations more reliable and helpful for people making decisions about AI models. |
Keywords
* Artificial intelligence * Clustering * Inference
