Summary of Fair Bilevel Neural Network (fairbinn): on Balancing Fairness and Accuracy Via Stackelberg Equilibrium, by Mehdi Yazdani-jahromi et al.
Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
by Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, AmirArsalan Rajabi, Aida Tayebi, Ivan Garibay, Ozlem Ozmen Garibay
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel methodology to mitigate bias in machine learning models, ensuring parity and equal treatment across diverse groups. The approach uses bilevel optimization principles to optimize for both accuracy and fairness objectives concurrently, achieving Pareto optimal solutions that balance these two goals. This method outperforms state-of-the-art fairness methods on tabular datasets like UCI Adult and Heritage Health, bridging the accuracy-fairness gap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make machine learning models fairer. It’s like balancing two goals: making sure the model is good at predicting things (accuracy) and making sure it treats everyone equally (fairness). The new method does this by using special math tricks that help find the best balance between accuracy and fairness. This means the model can be more accurate without being unfair to certain groups of people. |
Keywords
* Artificial intelligence * Machine learning * Optimization
