Summary of Alterfactual Explanations — the Relevance Of Irrelevance For Explaining Ai Systems, by Silvan Mertes et al.
Alterfactual Explanations – The Relevance of Irrelevance for Explaining AI Systems
by Silvan Mertes, Christina Karle, Tobias Huber, Katharina Weitz, Ruben Schlagowski, Elisabeth André
First submitted to arxiv on: 19 Jul 2022
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 In this paper, researchers introduce a new approach to Explainable Artificial Intelligence (XAI), called Alterfactual Explanations, which provides insights into how artificial intelligence systems make decisions. Unlike existing XAI methods that focus on important features, Alterfactual Explanations demonstrate the impact of irrelevant information on AI decision-making by presenting alternative scenarios where these features are altered. This approach is evaluated through a comprehensive user study, revealing significant improvements in users’ understanding of AI reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) makes decisions based on the data it receives. But have you ever wondered how AI systems think? A team of researchers has developed a new way to explain AI decision-making. Instead of just focusing on what’s important, they show how changing irrelevant information can affect an AI’s choice. This helps people understand AI better and make sense of its decisions. |
