Summary of Advancing Explainable Ai with Causal Analysis in Large-scale Fuzzy Cognitive Maps, by Marios Tyrovolas et al.
Advancing Explainable AI with Causal Analysis in Large-Scale Fuzzy Cognitive Maps
by Marios Tyrovolas, Nikolaos D. Kallimanis, Chrysostomos Stylios
First submitted to arxiv on: 15 May 2024
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 A novel approach to explainable AI (XAI) is proposed in this paper, which combines expert knowledge and data-driven insights using Fuzzy Cognitive Maps (FCMs). The “Total Causal Effect Calculation for FCMs” (TCEC-FCM) algorithm efficiently calculates total causal effects among concepts in large-scale FCMs, overcoming the challenge of exhaustive causal path exploration. This breakthrough enables the use of FCMs in modern complex XAI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make artificial intelligence more understandable is being developed. It uses something called Fuzzy Cognitive Maps (FCMs) which combine what experts know with what can be learned from data. This helps make AI models more transparent and easy to understand. The researchers came up with a new method to calculate how one thing affects another in large-scale FCMs, making it easier to use them for important applications. |
