Summary of Mind the Graph When Balancing Data For Fairness or Robustness, by Jessica Schrouff et al.
Mind the Graph When Balancing Data for Fairness or Robustness
by Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D’Amour, Silvia Chiappa
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new study investigates ways to ensure fairness and robustness in machine learning models by addressing undesired dependencies between variables. The researchers explore a common approach called data balancing, which aims to remove these dependencies. They define conditions under which data balancing leads to fair or robust models, but find that the resulting balanced distribution may not always effectively eliminate unwanted dependencies. This can lead to multiple failure modes and even interfere with other techniques used to mitigate bias. The study emphasizes the importance of considering the causal graph of a task before performing data balancing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data balancing is an important technique in machine learning that helps remove undesired dependencies between variables. A new study shows that this approach doesn’t always work as expected, and that we need to consider how our data relates to each other. This matters because it can affect whether our models are fair and robust. The study highlights the importance of understanding these relationships before trying to balance our data. |
Keywords
» Artificial intelligence » Machine learning
