Summary of Position: Cracking the Code Of Cascading Disparity Towards Marginalized Communities, by Golnoosh Farnadi et al.
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
by Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 paper examines the potential risks and inequalities associated with the rise of foundation models in AI, highlighting the interconnected disparities that may exacerbate existing problems for marginalized communities. The authors argue that these disparities are not isolated concerns but rather part of a cascading phenomenon that can have long-lasting negative consequences. They contrast foundation models with traditional models and emphasize the unique threats they pose to marginalized communities. The paper defines marginalized communities within the machine learning context and analyzes the sources of disparities, tracing them from data creation to deployment procedures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how some new AI technology could make things worse for people who are already struggling. It says that this technology can create problems that affect lots of people, not just a few. The authors think we should pay attention to these problems and try to fix them before they get out of control. |
Keywords
» Artificial intelligence » Attention » Machine learning
