# kaal:claim:4755632-036

**Claim.** Reputation scores on the ALE Platform balance supply and demand through a two-sided incentive: requesters with lower reputation scores find workers less likely to accept their offers, and workers with low reputation scores are less likely to be retained for micro task work.

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

**Holds when.**

- applies to the ALE Platform reputation scoring system

**Source quote.**

> If requesters have a lower reputation score, workers become less likely to accept requesters' offers. In turn, low reputation scores for micro task workers result in a lower likelihood of retention

**From.** Wulf A. Kaal, *AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning* (2024), Removing Cost of Micro Task Work Duplication, page 48

**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.** reputation, risk-and-incentives, economics

**Keywords.** reputation-scores, two-sided-incentives, market-equilibrium, micro-task-work

**Related claims.**

- specializes: https://wulfkaal.github.io/claims/4734750-037
- specializes: https://wulfkaal.github.io/claims/3128900-026

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