Summary of Explainable Artificial Intelligence: a Survey Of Needs, Techniques, Applications, and Future Direction, by Melkamu Mersha et al.
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
by Melkamu Mersha, Khang Lam, Joseph Wood, Ali AlShami, Jugal Kalita
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 Artificial intelligence (AI) models have long been criticized for their lack of transparency, making it challenging to establish trust in safety-critical domains such as healthcare, finance, and autonomous vehicles. Explainable Artificial Intelligence (XAI) seeks to address these concerns by providing interpretable explanations for AI model decisions and predictions. This paper reviews the current state of XAI research, including its fundamental concepts, general principles, and various techniques. The survey covers key terminology, the need for XAI, beneficiaries, a taxonomy of methods, and applications across different domains. The findings are aimed at researchers, practitioners, developers, and beneficiaries interested in enhancing AI model trustworthiness, transparency, accountability, and fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using artificial intelligence to make important decisions, but not being able to understand why it made those choices. This is a big problem, especially when it comes to things like healthcare or self-driving cars. To fix this, researchers have developed something called Explainable Artificial Intelligence (XAI). XAI helps us understand how AI models make decisions by providing clear explanations. In this paper, the authors review what we currently know about XAI and its many applications. They explore important topics such as why we need XAI, who benefits from it, and how different methods work. |
