Summary of Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production, by Patrick Oliver Schenk and Christoph Kern
Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production
by Patrick Oliver Schenk, Christoph Kern
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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 presents a quality framework for statistical algorithms that incorporates fairness as a crucial aspect in the development and deployment of Machine Learning (ML) solutions by National Statistical Organizations (NSOs). Building on the Quality Framework for Statistical Algorithms (QF4SA), the authors map its quality dimensions to algorithmic fairness, expanding the framework to emphasize the importance of fairness and its interactions with other dimensions. The paper demonstrates how this mapping can inform methodology in official statistics, algorithmic fairness, and trustworthy machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure that statistical algorithms are fair and accurate when using Machine Learning (ML). It’s like a set of guidelines to make sure that the numbers we use for important decisions are correct and don’t favor some groups over others. The authors took an existing framework and added fairness as one of its most important qualities. They showed how this can help with statistics, making sure algorithms are fair and trustworthy. |
Keywords
* Artificial intelligence * Machine learning
