Qualification: Privacy-Preserving and Accountable Multi-agent Learning
Anudit Nagar, Cuong Tran, Ferdinando Fioretto qualify Kaal's source-bound position through Privacy-Preserving and Accountable Multi-agent Learning. The indexed proposition states that this paper addresses these challenges and presents Privacy-preserving and Accountable Distributed Learning (PA-DL), a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protection of the agents data, and Ethereum smart contracts to ensure accountability. This bears on Kaal's claim that infrastructure level permissioning neglects critical risks such as smart contract exploits, bugs, and permission conflicts, and proposes no real time enforcement across distributed nodes, which weakens its claim to bridge AI autonomy and accountability. The proposed privacy-preserving, smart-contract-accountable architecture is a concrete response to the accountability gap Kaal identifies, but the abstract does not evaluate smart-contract exploits, permission conflicts, or real-time enforcement. The response is limited to the indexed proposition and does not imply review of the full external work.
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