Summary of Trusting Fair Data: Leveraging Quality in Fairness-driven Data Removal Techniques, by Manh Khoi Duong et al.
Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniques
by Manh Khoi Duong, Stefan Conrad
First submitted to arxiv on: 21 May 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 This paper addresses bias mitigation techniques in machine learning by proposing a framework that balances fairness and data quality. Specifically, it introduces a multi-objective optimization problem that considers both fairness and minimal data loss. The approach aims to provide users with Pareto-optimal solutions for selecting the most suitable dataset subset for their applications. The methodology is implemented as a Python package called FairDo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research paper explores ways to make machine learning models fairer by removing biased data from training sets. However, it also acknowledges that removing too much data can lead to less trustworthy results. To solve this problem, the authors propose a new approach that balances fairness and data quality, allowing users to choose the best dataset for their needs. |
Keywords
» Artificial intelligence » Machine learning » Optimization
