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 "name": "Reputation scores",
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  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4755632-036",
   "identifier": "kaal:claim:4755632-036",
   "text": "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.",
   "abstract": "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",
   "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "applies to the ALE Platform reputation scoring system"
   ],
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   "status": "current"
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  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4755632-037",
   "identifier": "kaal:claim:4755632-037",
   "text": "The reputation score mechanism helps discern malicious actors from simple mistakes and protects workers and verifiers from fraudulent requesters and suboptimally designed requests, so reputation serves as a bidirectional screening device rather than only a worker rating.",
   "abstract": "The reputation score mechanism helps discern malicious actors and simple mistakes. It also protects workers and verifiers from fraudulent requesters and suboptimally designed requests.",
   "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
   "datePublished": "2024",
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   "@id": "https://wulfkaal.github.io/claims/4755632-038",
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   "text": "Reputation based market dynamics lower the cost of duplication relative to centralized micro task work: where a reliable high reputation worker completes the task, duplication can fall from fifteen to five or fewer in a decentralized setup, which is what enables scaling of micro task work.",
   "abstract": "If a reliable and high reputation score worker completes the tasks, the duplication may be brought from 15 to 5 or less in the",
   "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "conditioned on a reliable and high reputation score worker completing the tasks"
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4755632-040",
   "identifier": "kaal:claim:4755632-040",
   "text": "Paying workers a share of incoming compensation pro rata to their reputation scores makes rigor self-enforcing, because workers who do not engage with the required care lose their spot in the reputation rankings and thereby lose their share of the job fee distribution.",
   "abstract": "they sacrifice their spot in the rankings of reputation scores which affects their participation in the job fee distribution, which is paid out pro rata to the respective reputation scores.",
   "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
   "datePublished": "2024",
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-029",
   "identifier": "kaal:claim:4855607-029",
   "text": "In federated learning, validation pools coordinated by smart contracts should dispense rewards pro rata to the reputation a node has accumulated through productive work, so that incentives track a node's actual contribution to the model's learning rather than mere participation.",
   "abstract": "with validation pools that are smart contract coordinated to dispense rewards pro rata to the reputation scores a node may have accumulated through productive work. This mechanism ensures that nodes are incentivized based on their actual input to the AI model's learning.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "design",
   "confidence": "argued",
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    "federated learning where node contribution quality varies and must be motivated"
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   "@id": "https://wulfkaal.github.io/claims/5225296-029",
   "identifier": "kaal:claim:5225296-029",
   "text": "Collusion, meaning coordinated action among validators to manipulate reputation scores or governance outcomes, threatens the fairness and integrity of SPoS independently of any cryptographic weakness.",
   "abstract": "Collusion risks—coordinated efforts among validators to manipulate reputation scores or governance outcomes—further threaten fairness and integrity",
   "citation": "Wulf A. Kaal, Cryptographic Foundations and Interdisciplinary Dimensions of the Secure Proof of Stake (SPoS) Conse (2025). SSRN: https://ssrn.com/abstract=5225296",
   "datePublished": "2025",
   "claim_type": "failure",
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 "description": "6 claims in the published works of Wulf A. Kaal carry the concept tag 'reputation-scores'. Derived node: a roster, not an adjudicated definition."
}