kaal:claim:4755632-041

Combining decentralized governance with gamification of micro task work is the condition under which gamification does not compromise dataset quality and accuracy, and this combination is what allows gamified micro task work to scale high-quality diverse datasets for AI learning.

combination of decentralized governance and gamification of micro task work, ALE Platform ensures that gamification techniques do not compromise the quality and accuracy

Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024), Gamification of Micro Task Work, p. 52
https://ssrn.com/abstract=4755632 · source PDF

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

• applies to the ALE Platform design

conditionsupport: argueddecentralizationgovernance-designempirical-evidenceai-and-agents

• extends kaal:claim:3128900-003 The performance of an AI neural network's learning algorithm during supervised training rises with the quality...
• extends kaal:claim:4734750-031 Supervised learning currently depends on labeled data produced by micro task work, so the evolution and growth...
• extended_by kaal:claim:6244278-033 Reputation-weighted micro-task systems can potentially reduce human task redundancy from ten-to-fifteen-fold t...
• extends kaal:claim:4734750-032 Because AI progress depends on labeled data from micro task work, the significant limitations of existing micr...

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