Summary of Debiasing Machine Learning Models by Using Weakly Supervised Learning, By Renan D. B. Brotto et al.
Debiasing Machine Learning Models by Using Weakly Supervised Learning
by Renan D. B. Brotto, Jean-Michel Loubes, Laurent Risser, Jean-Pierre Florens, Kenji Nose-Filho, João M. T. Romano
First submitted to arxiv on: 23 Feb 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 Machine learning educators can expect this paper to introduce a novel approach to mitigating bias in algorithmic decisions when both output and sensitive variables are continuous. The authors build upon existing work on discrete sensitive variables, which has limitations when dealing with age or financial status-based biases. Their proposed strategy is based on endogeneity from econometrics and uses weakly supervised learning to make predictions without requiring expert intervention. This model-agnostic approach utilizes a large amount of input observations and their corresponding predictions, with only a small fraction of true output predictions needed. The authors demonstrate the effectiveness of their method using synthetic data that mimics real-life applications in econometrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem! Right now, most algorithms make unfair decisions based on things like age or financial status because they were designed for discrete variables, not continuous ones. The authors came up with a new way to fix this by using something called endogeneity from economics. It’s like a weakly supervised learning method that can work with any algorithm and only needs a little bit of help from humans. They tested it on fake data that looks like real-life scenarios in economics, and it works really well! |
Keywords
* Artificial intelligence * Machine learning * Supervised * Synthetic data
