Summary of Standardness Clouds Meaning: a Position Regarding the Informed Usage Of Standard Datasets, by Tim Cech et al.
Standardness Clouds Meaning: A Position Regarding the Informed Usage of Standard Datasets
by Tim Cech, Ole Wegen, Daniel Atzberger, Rico Richter, Willy Scheibel, Jürgen Döllner
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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 This paper critiques the uncritical use of standard machine learning (ML) datasets, highlighting a lack of discussion on how their labels match derived categories for specific use cases. By reviewing recent literature using standard datasets, the authors find that these datasets’ “standardness” clouds their actual coherency and applicability, hindering trust in ML models trained on them. The authors argue against relying solely on standard datasets and instead advocate for critical examination through methods like Grounded Theory and Hypotheses Testing through Visualization. They demonstrate this approach by analyzing the 20 Newsgroups dataset and MNIST dataset, both considered standard in their respective domains. The results show that the 20 Newsgroups dataset’s labels are imprecise, implying no meaningful abstraction or conclusions can be drawn from achieving high accuracy on this dataset. For the MNIST dataset, they confirm that labels are well-defined. The authors conclude that assessing quality and suitability is essential to learn meaningful abstractions and improve trust in ML models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how machine learning (ML) models are trained using standard datasets. But do we really know if these datasets are accurate? Researchers found that many people just assume the datasets are good without checking. This can lead to problems because the ML models might not be very reliable. The authors suggest a new way to check the accuracy of these datasets, called Grounded Theory and Hypotheses Testing through Visualization. They tested this method on two famous datasets: 20 Newsgroups and MNIST. The results show that one dataset has bad labels, making it hard to learn anything from it. The other dataset has good labels, but you still need to check if they’re accurate. The authors think we should be more careful when using standard datasets. |
Keywords
» Artificial intelligence » Machine learning
