Summary of Experiments with Truth Using Machine Learning: Spectral Analysis and Explainable Classification Of Synthetic, False, and Genuine Information, by Vishnu S. Pendyala and Madhulika Dutta
Experiments with truth using Machine Learning: Spectral analysis and explainable classification of synthetic, false, and genuine information
by Vishnu S. Pendyala, Madhulika Dutta
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The paper investigates why misinformation remains a persistent issue, despite years of research and various proposed solutions. It analyzes synthetic, false, and genuine text data using spectral analysis, visualization, and explainability methods to understand the problem’s root causes. The study employs embedding techniques on multiple datasets, including t-SNE, PCA, and VAEs, followed by classification using various machine learning algorithms. Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Integrated Gradients are used to explain the classification results. The findings suggest that misinformation is tightly linked with genuine information, and machine learning algorithms may not be as effective in separating the two as previously claimed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to figure out why we still have a big problem with false information on the internet, even though many experts have been working on solutions for a long time. They looked at fake, real, and made-up text data using special tools that show patterns and relationships. The researchers used different ways to represent this data, like t-SNE and PCA, and then tried to identify patterns using machine learning algorithms. They also used explanations like LIME, SHAP, and Integrated Gradients to understand why the algorithms were making certain decisions. What they found is that false information is often mixed up with real information, and even the best algorithms have a hard time telling them apart. |
Keywords
» Artificial intelligence » Classification » Embedding » Machine learning » Pca
