Summary of Sharp: a Novel Feature Importance Framework For Ranking, by Venetia Pliatsika and Joao Fonseca and Kateryna Akhynko and Ivan Shevchenko and Julia Stoyanovich
ShaRP: A Novel Feature Importance Framework for Ranking
by Venetia Pliatsika, Joao Fonseca, Kateryna Akhynko, Ivan Shevchenko, Julia Stoyanovich
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed ShaRP-Shapley Values for Rankings and Preferences framework addresses the need for explainable ranking methods in high-stakes domains like hiring, college admissions, and lending. By recognizing the limitations of existing explainability methods like SHAP, which are geared towards classification and regression tasks, this paper presents a novel approach to understanding the contributions of features to ranked outcomes. This is particularly important given the significant impact that ranking decisions can have on individuals, organizations, and broader population groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re applying for a job or trying to get into your dream college. You wonder how the decision-makers came up with their ranking for you. In this paper, researchers explore why current methods aren’t enough to explain these rankings. They propose a new approach that helps us understand what factors contribute most to our ranking position. |
Keywords
» Artificial intelligence » Classification » Regression
