Summary of Foundations For Unfairness in Anomaly Detection — Case Studies in Facial Imaging Data, by Michael Livanos and Ian Davidson
Foundations for Unfairness in Anomaly Detection – Case Studies in Facial Imaging Data
by Michael Livanos, Ian Davidson
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 intersection of deep anomaly detection (AD) and facial imaging data, specifically examining the fairness of these algorithms in identifying entities for further investigation or exclusion. The authors investigate two main categories of AD algorithms: autoencoder-based and single-class-based, and experimentally verify sources of unfairness such as under-representation, spurious group features, and labeling noise. They find that a lack of compressibility is not the primary cause of unfairness, but rather a natural hierarchy amongst these sources exists. The study highlights the importance of understanding why deep AD algorithms are unfairly targeting certain groups, such as men of color in portraits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how AI can be used to identify unusual people in pictures, and whether this process is fair. It finds that current methods often unfairly pick out certain groups of people, like men of color, and tries to figure out why this happens. The study shows that there are different reasons for this unfairness, including the fact that some groups are underrepresented in the data, or have unique features that make them stand out. The researchers conclude that it’s important to understand why these biases happen so we can work towards making AI more fair. |
Keywords
» Artificial intelligence » Anomaly detection » Autoencoder
