Summary of Fairness, Accuracy, and Unreliable Data, by Kevin Stangl
Fairness, Accuracy, and Unreliable Data
by Kevin Stangl
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 thesis explores three areas to improve reliability: fairness, strategic classification, and algorithmic robustness. The research focuses on understanding how classical learning theory assumptions can be misaligned with real-world data distributions, leading to ineffective or misleading algorithms. This knowledge can inform best practices and guide the design of reliable systems. The study investigates these domains’ specific properties and structures that complicate learning, seeking to develop effective models for strategic classification, fairness in machine learning, and robust algorithmic performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This thesis aims to improve the reliability of machine learning by studying three key areas: fairness, strategic classification, and algorithmic robustness. The research looks at how real-world data distributions can be different from classical learning theory assumptions, leading to problems with algorithms. By understanding these differences, the study hopes to develop reliable systems that work well in practice. |
Keywords
» Artificial intelligence » Classification » Machine learning
