Summary of Statistical Learning For Constrained Functional Parameters in Infinite-dimensional Models with Applications in Fair Machine Learning, by Razieh Nabi et al.
Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning
by Razieh Nabi, Nima S. Hejazi, Mark J. van der Laan, David Benkeser
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG); Methodology (stat.ME)
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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 studies constrained statistical machine learning, specifically focusing on algorithmic fairness in predictive models. The authors develop a framework for characterizing the optimal constrained parameter as the minimizer of a penalized risk criterion using Lagrange multipliers. They demonstrate that closed-form solutions are often available, providing insight into mechanisms driving fairness in predictive models. Additionally, they propose an estimation procedure for constructing fair machine learning algorithms that can be applied with any statistical learning approach and off-the-shelf software. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make sure machine learning models are fair when making predictions. Right now, there are many different ways to define what fairness means, but the authors of this paper want to create a general framework for understanding how to achieve fairness in predictive models. They show that by using something called Lagrange multipliers, they can find the best way to make sure their model is fair while still being good at making predictions. |
Keywords
» Artificial intelligence » Machine learning
