Summary of Multi-criteria Comparison As a Method Of Advancing Knowledge-guided Machine Learning, by Jason L. Harman and Jaelle Scheuerman
Multi-Criteria Comparison as a Method of Advancing Knowledge-Guided Machine Learning
by Jason L. Harman, Jaelle Scheuerman
First submitted to arxiv on: 18 Mar 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 This paper proposes a novel method for evaluating AI/ML models, which can be applied to assess various criteria including scientific principles and practical outcomes. The approach emerges from prediction competitions in Psychology and Decision Science, where it was used to evaluate multiple candidate models of varying types and structures. The evaluation process involves ordinal ranking of criteria scores using voting rules from computational social choice, enabling the comparison of diverse models and measures. This holistic evaluation method has additional advantages and applications discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to test and compare artificial intelligence (AI) and machine learning (ML) models. The authors developed a new way to evaluate these models based on different criteria, like scientific principles and practical outcomes. They tested this approach in competitions with experts from psychology and decision science. This evaluation method can be used to compare many different models and help us understand what works well and what doesn’t. |
Keywords
* Artificial intelligence * Machine learning
