Summary of Fairness Analysis with Shapley-owen Effects, by Harald Ruess
Fairness Analysis with Shapley-Owen Effects
by Harald Ruess
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 proposes a novel approach to measuring fairness in machine learning models by decomposing the computation of Shapley-Owen effects into model-specific and model-independent parts. The authors argue that relative importance and its equitable attribution are key to understanding fairness, and develop an algorithm for computing precise and sparse truncations of polynomial chaos expansions (PCE) and spectral decompositions of Shapley-Owen effects. The approximations converge to their true values, enabling efficient computation of fairness metrics. This research has implications for the development of fair AI models in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about making sure that artificial intelligence systems are fair. They want to figure out how important each part is in a complex system. To do this, they break it down into two parts: one that depends on the model and one that doesn’t. This helps them understand what’s fair and what isn’t. They also developed a way to get an approximate answer for fairness quickly, which will help with building more fair AI systems. |
Keywords
» Artificial intelligence » Machine learning
