Summary of Fairness in Algorithmic Recourse Through the Lens Of Substantive Equality Of Opportunity, by Andrew Bell et al.
Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
by Andrew Bell, Joao Fonseca, Carlo Abrate, Francesco Bonchi, Julia Stoyanovich
First submitted to arxiv on: 29 Jan 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 Machine learning has made significant strides in providing algorithmic recourse, offering recommendations to individuals affected by AI-driven outcomes on how they can take action and alter that outcome. While recent work has shown that even fair AI decision-making classifiers can lead to unfair recourse due to initial circumstance disparities, there is a pressing need for more comprehensive methods and metrics to evaluate fairness in recourse, taking into account time. Time plays a crucial role as it affects the setting, making model or data drift a critical consideration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) systems are getting smarter at making decisions, but what happens when those decisions affect us negatively? Recently, researchers have been working on creating “algorithmic recourse” – ways for people to take action and change the outcome. However, new studies show that even if AI is fair, the way we respond can be unfair too. This can create bigger problems for groups who are already disadvantaged. To fix this, scientists need to develop better methods to measure fairness when making changes. |
Keywords
* Artificial intelligence * Machine learning
