Summary of Resource-constrained Fairness, by Sofie Goethals et al.
Resource-constrained Fairness
by Sofie Goethals, Eoin Delaney, Brent Mittelstadt, Chris Russell
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a new approach to fair machine learning, addressing the issue of resource constraints in deploying machine learning systems. Traditional methods ignore these limitations, making them unsuitable for real-world deployment. The researchers introduce the concept of “resource-constrained fairness” and quantify the cost of fairness within this framework. They show that the level of available resources significantly influences this cost, a factor often overlooked in previous evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper is about making machine learning systems fair when there are limited resources available. Imagine you’re a doctor trying to schedule patients for follow-up appointments with specialists, but you only have so many slots available. You want to make sure everyone gets treated fairly, but you can’t just give every patient an appointment. This is the problem this paper tries to solve. |
Keywords
» Artificial intelligence » Machine learning
