Summary of Using Machine Bias to Measure Human Bias, by Wanxue Dong et al.
Using Machine Bias To Measure Human Bias
by Wanxue Dong, Maria De-Arteaga, Maytal Saar-Tsechansky
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 The proposed machine learning-based framework aims to assess bias in human-generated decisions when gold standard labels are scarce. The method provides theoretical guarantees and empirical evidence demonstrating its superiority over existing alternatives. This methodology establishes a foundation for transparency in human decision-making, with substantial implications for managerial duties and potential for alleviating algorithmic biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning-based framework helps organizations measure bias in human decisions when there’s no clear “right” answer. This is important because biased decisions can harm people and lead to unfair outcomes. The framework is better than existing methods and provides a way to be more transparent about decision-making, which could help reduce biases in algorithms that are trained using human decisions. |
Keywords
* Artificial intelligence * Machine learning
