Summary of Dataset | Mindset = Explainable Ai | Interpretable Ai, by Caesar Wu et al.
Dataset | Mindset = Explainable AI | Interpretable AI
by Caesar Wu, Rajkumar Buyya, Yuan Fang Li, Pascal Bouvry
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 medium-difficulty summary: This paper explores the differences between Explainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI). While both aim to provide transparency, clarity, fairness, reliability, and accountability in ethical AI and trustworthy AI (TAI), XAI focuses on post-hoc analysis of a dataset, whereas IAI requires an a priori mindset of abstraction. The authors argue that IAI is broader than XAI, encompassing not only the domain of a dataset but also the domain of a mindset. They propose empirical experiments to prove their hypothesis using open datasets and High-Performance Computing (HPC). This distinction between XAI and IAI is crucial for determining regulatory policies in various AI applications, including healthcare, human resources, banking, and finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: Imagine trying to understand why a computer program made a certain decision. Explainable Artificial Intelligence (XAI) helps us do just that by looking at the data used to make the decision. Interpretable Artificial Intelligence (IAI) goes further by considering the mindset behind the decision-making process. The authors of this paper think these two concepts are important because they can help create more trustworthy AI systems. They want to clarify the differences between XAI and IAI so that we can use them to make better decisions in fields like healthcare, finance, and education. |
