Summary of Exploring Bias and Prediction Metrics to Characterise the Fairness Of Machine Learning For Equity-centered Public Health Decision-making: a Narrative Review, by Shaina Raza et al.
Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review
by Shaina Raza, Arash Shaban-Nejad, Elham Dolatabadi, Hiroshi Mamiya
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper investigates the limitations of applying Machine Learning (ML) in public health research, highlighting algorithmic bias and systematic errors in predicted population health outcomes. To address this gap, the study reviews the types of bias generated by ML and proposes quantitative metrics for assessing these biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is a powerful tool that can help us make better decisions about our health. But when we use it to predict things like how many people might get sick or what will happen if we take certain actions, we need to be careful not to introduce mistakes into the system. This paper looks at some of the common mistakes that ML models can make and proposes ways to measure them. |
Keywords
» Artificial intelligence » Machine learning
