Summary of Mapping the Potential Of Explainable Ai For Fairness Along the Ai Lifecycle, by Luca Deck et al.
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle
by Luca Deck, Astrid Schomäcker, Timo Speith, Jakob Schöffer, Lena Kästner, Niklas Kühl
First submitted to arxiv on: 29 Apr 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 explores the relationship between artificial intelligence (AI) systems and algorithmic fairness in high-stakes scenarios, highlighting the need for improved fairness measures across the AI lifecycle. It identifies eight fairness desiderata and maps them along the AI lifecycle, discussing how explainable AI (XAI) can aid in addressing each desideratum. The paper aims to provide a framework for practical applications and inspire XAI research focused on these fairness goals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are looking at how artificial intelligence can be fairer. They want to make sure that AI systems don’t unfairly treat certain groups of people. To do this, they’re examining eight different ideas about what fairness means in AI. They’re also trying to figure out how a technique called explainable AI (XAI) can help make AI systems fairer. |
