Summary of Group-blind Optimal Transport to Group Parity and Its Constrained Variants, by Quan Zhou et al.
Group-blind optimal transport to group parity and its constrained variants
by Quan Zhou, Jakub Marecek
First submitted to arxiv on: 17 Oct 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 A machine learning paper presents a new fairness approach for addressing protected attributes, such as gender or race, without requiring explicit values for individual samples. The authors design a group-blind projection map that aligns feature distributions between privileged and unprivileged groups in the source data, achieving demographic parity. This is achieved by utilizing feature distributions from both groups in a broader population, assuming unbiased representation of the population. The approach is tested on synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning paper helps make algorithms fairer for people with protected attributes like gender or race. Without needing to know specific information about individuals, this method uses overall patterns to make sure different groups are treated equally. It works by looking at how features like height or weight are distributed across different groups and adjusting the data so that everyone has an equal chance of being chosen. This approach was tested on fake and real data. |
Keywords
* Artificial intelligence * Machine learning
