# kaal:claim:4755632-008

**Claim.** 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.

**Type.** mechanism  **Support.** argued

**Holds when.**

- applies to the supervised learning training phase

**Source quote.**

> The higher the quality and quantity of such labeled datasets the better the AI neural network's learning algorithm during the supervised training process.

**From.** Wulf A. Kaal, *AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning* (2024), Shortcomings in Legacy Micro Task Market, page 21

**Cite as.** Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632

**Verify.** sha256 of source PDF `972ccebf0c06ac1767a9e443bb95942b7670e806a63c25ee817c368a64c8eca8` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Learning%20-%20Decentralized%20Governance%20to%20Optimize%20Human%20Output%20Datasets%20for%20AI%20Learning.pdf

**Topics.** education-and-practice

**Keywords.** labeled-data, supervised-learning, micro-task-work, training-quality

**Related claims.**

- restates: https://wulfkaal.github.io/claims/3128900-003

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.