Summary of Diversified Ensembling: An Experiment in Crowdsourced Machine Learning, by Ira Globus-harris et al.
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
by Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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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 In this paper, researchers explored alternative crowdsourcing frameworks for fair machine learning, where participants specialize in specific subproblems to address subgroup unfairness. Unlike traditional crowdsourced ML, this approach allows participants to focus on their strengths and expertise, leading to diversification of efforts and potential participation from a broader range of individuals. The authors presented the first medium-scale experimental evaluation of this framework, with 46 teams attempting to predict income from American Community Survey data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to help machines learn without hurting certain groups of people. One way to do this is by letting many people work together on small parts of a big problem. This approach helps make sure the machine learning models are fair and don’t favor one group over another. In this study, 46 teams worked together to predict income based on census data. The researchers looked at how these teams approached the task and developed a special system to help them work together effectively. |
Keywords
* Artificial intelligence * Machine learning
