Summary of Demopts: Fairness Corrections in Covid-19 Case Prediction Models, by Naman Awasthi et al.
DemOpts: Fairness corrections in COVID-19 case prediction models
by Naman Awasthi, Saad Abrar, Daniel Smolyak, Vanessa Frias-Martinez
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 fairness of COVID-19 forecasting models that utilize multimodal data, such as mobility and socio-demographic information. These models often rely on deep learning techniques to predict case numbers and inform decision-making regarding resource allocation and intervention strategies. However, previous work has revealed biases in reported cases and sampling methods, which can affect the accuracy and fairness of predictions along racial and ethnic lines. This study shows that state-of-the-art deep learning models exhibit significant mean prediction errors across different racial and ethnic groups, potentially supporting unfair policy decisions. To address this issue, the authors propose a novel de-biasing method called DemOpts, which aims to increase the fairness of deep learning-based forecasting models trained on biased datasets. The results demonstrate that DemOpts achieves better error parity compared to other state-of-the-art de-biasing approaches, effectively reducing mean error distributions across different racial and ethnic groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well COVID-19 prediction models do in forecasting case numbers based on different types of data. The models are important because they help decision-makers decide where to allocate resources like hospital beds or order people to stay home. However, some studies have shown that the way cases are reported and the way we collect mobility data can be biased against certain racial and ethnic groups. This bias can affect how well the predictions work for those groups. The study finds that current models are not fair and may lead to unfair decisions. To fix this problem, the authors suggest a new method called DemOpts that helps make the predictions more fair. |
Keywords
» Artificial intelligence » Deep learning
