Summary of Glance: Global Actions in a Nutshell For Counterfactual Explainability, by Loukas Kavouras et al.
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
by Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas, Dimitrios Rontogiannis, Nikolaos Theologitis, Dimitris Sacharidis, Giorgos Giannopoulos, Dimitrios Tomaras, Kleopatra Markou, Dimitrios Gunopulos, Dimitris Fotakis, Ioannis Emiris
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposes a novel framework called GLANCE, which consists of two algorithms, C-GLANCE and T-GLANCE, designed to provide global counterfactual explanations for machine learning models. The goal is to balance three key objectives: maximizing effectiveness, minimizing cost, and maintaining interpretability. The authors introduce these concepts as actions that offer recourse, aiming to provide succinct explanations and insights applicable to large population subgroups. They measure the effectiveness by the fraction of the population provided with recourse, ensuring that the actions benefit as many individuals as possible while keeping the cost low. GLANCE’s primary challenge lies in balancing these trade-offs, which is achieved through a tunable parameter controlling the size of the actions. The framework demonstrates greater robustness and performance compared to existing methods across various datasets and models. This paper presents a versatile and adaptive approach for counterfactual explainability, addressing the need for effective global explanations in machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to explain how machines make decisions. It’s like trying to understand why someone chose a particular option, but instead of people, it’s about machines. The goal is to provide simple and easy-to-understand explanations that cover large groups of people. This helps ensure that the decisions made by machines are fair and reasonable. The authors propose a framework called GLANCE that can balance three important things: making sure the explanations are good, keeping the cost low, and keeping the number of explanations small so they’re easy to understand. They tested this approach on different datasets and models and found it performed better than other methods. |
Keywords
» Artificial intelligence » Machine learning
