Summary of Abroca Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation, by Conrad Borchers and Ryan S. Baker
ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation
by Conrad Borchers, Ryan S. Baker
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 studies the statistical properties of a fairness measure called Absolute Between-ROC Area (ABROCA), which quantifies group-level differences in classifier performance. ABROCA is particularly useful for detecting nuanced performance differences even when overall AUC values are similar. The authors sample ABROCA under various conditions and find that its distributions exhibit high skewness dependent on sample sizes, AUC differences, and class imbalance. This skewness can inflate ABROCA values by chance, even when data is drawn from populations with equivalent ROC curves. Therefore, the authors suggest that ABROCA requires careful interpretation given its distributional properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a way to measure if a computer program is biased or unfair. It’s called Absolute Between-ROC Area (ABROCA). The researchers studied how well this method works and found some problems with it. They simulated different scenarios and saw that the results of ABROCA can be very uneven, which means it might not always give an accurate answer. This is important to know when trying to figure out if a computer program is fair or not. |
Keywords
» Artificial intelligence » Auc
