Summary of Analyzing Fairness Of Classification Machine Learning Model with Structured Dataset, by Ahmed Rashed et al.
Analyzing Fairness of Classification Machine Learning Model with Structured Dataset
by Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel investigation into the fairness of machine learning (ML) models is presented, focusing on classification tasks with structured datasets. The study highlights the potential for biased predictions to perpetuate systemic inequalities, particularly in domains like healthcare, finance, education, and law enforcement where ML algorithms are increasingly relied upon. By analyzing a publicly available dataset from Kaggle, the research evaluates fairness in machine learning workflows, demonstrating the importance of addressing these concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is used to make important decisions in many areas, but it can sometimes be unfair or biased. This paper looks at how well machine learning models work when they’re applied to structured data and classification tasks. It shows that if ML models are biased, they might make predictions that keep systemic inequalities going. The researchers use a real dataset from Kaggle to test their ideas. |
Keywords
» Artificial intelligence » Classification » Machine learning
