Summary of On Explaining Unfairness: An Overview, by Christos Fragkathoulas et al.
On Explaining Unfairness: An Overview
by Christos Fragkathoulas, Vasiliki Papanikou, Danae Pla Karidi, Evaggelia Pitoura
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 paper explores the interconnectedness of algorithmic fairness and explainability, two crucial aspects of responsible AI development. The authors present comprehensive taxonomies for both fairness and explanations, identifying three types of explanations for fairness: enhancing metrics, understanding causes of unfairness, and designing methods to mitigate it. By examining these taxonomies, researchers can uncover gaps in existing literature, guiding future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how AI can be fair and easy to understand. It’s like a puzzle where we need to make sure the pieces fit together correctly. The authors looked at two important parts: making sure AI is fair and explaining why it makes certain decisions. They created lists of different types of explanations, showing us that there are many ways to approach fairness and understanding. By doing this research, scientists can find missing pieces in the puzzle, helping them make new discoveries. |
