Summary of Properties Of Fairness Measures in the Context Of Varying Class Imbalance and Protected Group Ratios, by Dariusz Brzezinski et al.
Properties of fairness measures in the context of varying class imbalance and protected group ratios
by Dariusz Brzezinski, Julia Stachowiak, Jerzy Stefanowski, Izabela Szczech, Robert Susmaga, Sofya Aksenyuk, Uladzimir Ivashka, Oleksandr Yasinskyi
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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 The paper investigates the properties of group fairness measures, specifically focusing on how they respond to changing class proportions and protected group distributions. It analyzes six popular measures (Equal Opportunity, Positive Predictive Parity, etc.) using probability mass functions, showing that some measures are more sensitive to class imbalance than others. This work can inform the selection of suitable fairness measures for real-world classification problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how machine learning models can be fair and avoid making biased decisions. Right now, many important systems like hiring tools or loan lenders rely on these models. But we need to make sure they don’t discriminate against certain groups. The researchers looked at six different ways to measure fairness, seeing how well they work when the data is unbalanced (more examples of one group than another). They found that some measures are better than others in dealing with this imbalance. This helps us choose the right tools for making fair decisions. |
Keywords
» Artificial intelligence » Classification » Machine learning » Probability
