Datasets Have Worldviews

Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too.

Datasets Have Worldviews Explorables Datasets Have Worldviews Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too. Suppose you have a dataset of shapes. They can either be shaded or unshaded . They look something like this: You built a supervised machine learning classifier that will automatically classify each shape as shaded or unshaded . You call it the “Is-Shaded Classifier”. Click “Run Classifier” to see how your model performs. It’s not perfect— some of the shapes are definitely misclassified. You want to improve your model

related reading

• Thinking about High-Quality Human Data | Lil'Loglilianweng.github.io
• The Limits of Dataissues.org
• Statistical Fairness - Turing Commonsalan-turing-institute.github.io
• Relative notions of fairnessfairmlbook.org
• Uncovering Neural Geometry in Vision Models With Block-Sparse Featurizersgoodfire.ai
• Deep Learning is Not So Mysterious or Differentarxiv.org
• LLMs and World Models, Part 1 - by Melanie Mitchellaiguide.substack.com
• Why you need to improve your training data, and how to do it << Pete Warden&#039;s blogpetewarden.com
• A Course in Machine Learningciml.info
• Scaling Laws Do Not Scalearxiv.org
• Hidden Biaspair.withgoogle.com
• Troubles with the Bias-Variance tradeoffejenner.com