Summary of On the Consistency Of Fairness Measurement Methods For Regression Tasks, by Abdalwahab Almajed et al.
On the Consistency of Fairness Measurement Methods for Regression Tasks
by Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
First submitted to arxiv on: 19 Jun 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 The proposed study aims to investigate the consistency of various fairness measurement methods in the regression domain. The authors recognize that existing metrics are computationally tractable in classification settings but become intractable when applied to regression tasks, leading to a lack of understanding about their consistency. To address this challenge, the researchers conducted an extensive set of experiments on different regression tasks and found that while some methods showed strong consistency across various tasks, others exhibited poor consistency in certain contexts. This highlights the need for a more principled approach to measuring fairness in the regression domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well different ways of measuring fairness work together in the context of machine learning. It’s important to make sure that machine learning models are fair, and right now there isn’t a clear way to measure this when we’re using regression instead of classification. The researchers tested several methods on many different tasks and found that some methods agree with each other more than others. This means we need a better approach to measuring fairness in the real world. |
Keywords
» Artificial intelligence » Classification » Machine learning » Regression
