Summary of Relevance-aware Algorithmic Recourse, by Dongwhi Kim et al.
Relevance-aware Algorithmic Recourse
by Dongwhi Kim, Nuno Moniz
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Relevance-Aware Algorithmic Recourse (RAAR) framework addresses the need for transparent and explainable machine learning models by leveraging the concept of relevance in regression tasks. The framework aims to provide actionable insights to alter outcomes by answering “What do I have to change?” to achieve a desired result. Unlike current methods, RAAR treats domain values differently, making it more realistic in real-world settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting better at predicting things, but we need to make sure these models are fair and don’t hurt certain groups of people. One way to do this is by understanding why the model made a decision. The “algorithmic recourse” tool helps us figure out what we can change to get a different outcome. The problem is that current methods treat all information equally, which isn’t how things work in real life. This paper proposes a new way of doing algorithmic recourse called RAAR, which looks at the importance or relevance of the data when making decisions. We tested this method on 15 datasets and found that it works as well as other popular approaches, but is more efficient and cost-effective. |
Keywords
» Artificial intelligence » Machine learning » Regression
