Summary of Data Augmentation Via Diffusion Model to Enhance Ai Fairness, by Christina Hastings Blow et al.
Data Augmentation via Diffusion Model to Enhance AI Fairness
by Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon, Xishuang Dong
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 explores the potential of diffusion models, particularly the Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM), to generate synthetic tabular data and improve AI fairness. The authors use Tab-DDPM with different amounts of generated data for data augmentation and reweighting samples from AIF360 to enhance AI fairness. They validate their approach using five traditional machine learning models: Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest. Experimental results show that the synthetic data improves fairness in binary classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence fairer by creating fake data to train AI systems. The researchers use a special kind of model called a diffusion model to generate this fake data. They test their approach with different amounts of generated data and find that it makes AI systems more fair. This is important because AI systems can sometimes be biased against certain groups of people. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Decision tree » Diffusion » Diffusion model » Logistic regression » Machine learning » Naive bayes » Probabilistic model » Random forest » Synthetic data
