Summary of Fantastic Biases (what Are They) and Where to Find Them, by Valentin Barriere
Fantastic Biases (What are They) and Where to Find Them
by Valentin Barriere
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG)
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 The paper explores the concept of bias in machine learning models, particularly in deep learning systems that learn from massive datasets. As AI becomes increasingly pervasive, its impact on society depends on fair and universal use, but this requires addressing biases that can perpetuate inequalities. The authors define bias, demystify its causes, and identify common types, before presenting methods to detect and mitigate these biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning models can learn patterns from big data, but also pick up on unfair correlations. This is a problem because AI has the power to shape society, so we need to make sure it’s not just reproducing existing inequalities. The authors try to understand what bias is and why it happens, then show some common types of bias that people don’t want in their models. Finally, they talk about how to find and fix these biases. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
