# Supervised learning

`kaal:entity:supervised-learning`

**Status.** derived

This node is assembled mechanically from the 4 claims that carry the concept tag `supervised-learning`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

4 claims across 4 works, 2018 to 2025.

**2018**

- [3128900-002](https://wulfkaal.github.io/claims/3128900-002) [mechanism/argued] -- Supervised machine learning currently depends on labelled data produced by micro task work, because unsupervised and reinforcement learning remain more complex and less relied upon for AI development.
  > While this may change as unsupervised and reinforcement learning evolve, currently, supervised learning depends on labelled data that is produced via micro task work.
  Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900

**2024**

- [4734750-031](https://wulfkaal.github.io/claims/4734750-031) [condition/argued] -- Supervised learning currently depends on labeled data produced by micro task work, so the evolution and growth of AI is correlated with the evolution and growth of micro task work.
  > currently, supervised learning depends on labeled data that is produced via micro task work
  Wulf A. Kaal, Code Review DAO (2024). SSRN: https://ssrn.com/abstract=4734750
- [4755632-008](https://wulfkaal.github.io/claims/4755632-008) [mechanism/argued] -- The quality and quantity of labeled datasets directly governs the performance of the neural network's learning algorithm during supervised training, so the evolution of AI is correlated with the evolution of micro task work.
  > The higher the quality and quantity of such labeled datasets the better the AI neural network's learning algorithm during the supervised training process.
  Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632

**2025**

- [5541658-006](https://wulfkaal.github.io/claims/5541658-006) [empirical/evidenced] -- Support Vector Machine models reached 96.9% accuracy in classifying semantic biases in judicial judgments on the Chinese AI and Law dataset, outperforming Naive Bayes, multi-layer perceptron, and K-nearest neighbor classifiers.
  > as demonstrated by a study on the Chinese AI and Law (CAIL) dataset where SVM models reached 96.9% accuracy in classifying semantic biases in judicial judgments.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/supervised-learning.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.