Summary of Optimisation Strategies For Ensuring Fairness in Machine Learning: with and Without Demographics, by Quan Zhou
Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics
by Quan Zhou
First submitted to arxiv on: 13 Nov 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 The proposed research introduces two novel frameworks for addressing open questions in machine learning fairness. Building on existing work, the authors provide a comprehensive overview of the machine learning fairness landscape, highlighting its evolution as a critical concern in AI development. The paper’s contributions include formalizing two key aspects: (1) fairness metrics and (2) decision-making processes. By presenting these frameworks, the authors aim to advance our understanding of fair machine learning, ultimately enabling more transparent and equitable AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make sure AI is fair by introducing new ways to think about fairness in machine learning. The research provides a big-picture view of the field, showing how it has grown as an important issue in developing AI. The authors also present two important ideas: (1) measuring fairness and (2) making fair decisions. By doing this, they hope to improve our understanding of what makes AI fair and good for everyone. |
Keywords
* Artificial intelligence * Machine learning
