Summary of Is It Still Fair? a Comparative Evaluation Of Fairness Algorithms Through the Lens Of Covariate Drift, by Oscar Blessed Deho et al.
Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift
by Oscar Blessed Deho, Michael Bewong, Selasi Kwashie, Jiuyong Li, Jixue Liu, Lin Liu, Srecko Joksimovic
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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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 This paper tackles a crucial issue in machine learning: ensuring fairness despite naturally occurring changes in data patterns (data distributional drift). The authors investigate how drift affects 4 baseline algorithms, 7 fairness-aware algorithms, and their performance across 5 datasets. They find that drift can lead to unfairness, even in “fair” models, and that the choice of algorithm and training is impacted by this phenomenon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For high school students or non-technical adults, this paper explores a problem with machine learning: how data patterns change over time. This can affect algorithms designed to be fair. The authors test different approaches on various datasets and find that these changes can make some “fair” models unfair. They also show that the way we choose and train algorithms is affected by these changes. |
Keywords
* Artificial intelligence * Machine learning
