The PRODAO ecosystem integrates artificial intelligence (AI) not as a mere add-on feature but as a fundamental driving force that creates the platform's core value, innovates the user experience, and maximizes the security and efficiency of the network. In particular, considering PRODAO's characteristic that user data is decentralized and preserved in a mobile Web3 P2P environment, AI is not a simple auxiliary tool but an important driving force that realizes PRODAO's core values of “enhancing data value” and “accompanying experts' growth.”
5.1. The Role of AI in the PRODAO Ecosystem and Its Fields of Application
AI performs diverse roles across the entire PRODAO ecosystem and is applied in the following core fields.
- Value enhancement and evaluation: Objectively evaluates and optimizes the value of distributed data, expert knowledge, content, and the AI models themselves, maximizing the efficiency of the PoC (Proof of Contribution) algorithm.
- Service optimization and personalization: Innovates the user experience and promotes interaction within the ecosystem through personalized information provision and efficient resource matching.
- Network security and integrity maintenance: Contributes to securing the safety and reliability of the network through abuse detection, anomaly detection, data-reliability verification, and the like.
- Knowledge discovery and insight extraction: Discovers new patterns and insights from vast, decentralized knowledge and data to support experts' decision-making.
5.2. Using AI to Enhance Data Value
Using AI, PRODAO maximizes the potential value of distributed data and builds a virtuous cycle that returns this value back to the ecosystem.
- Automated data curation and refinement: AI analyzes data collected through the P2P network or provided by users (metadata, attribute attestations, etc.) to automatically classify, tag, and structure it, and it contributes to refining data by identifying duplicate or abnormal data. This increases the efficiency of the data marketplace.
- Data value-evaluation model: AI algorithms analyze diverse attributes of data—such as its uniqueness, scarcity, freshness, accuracy, frequency of use, and provenance (DID/VC-based)—to calculate an objective value score for each dataset. This score is used as a core metric of the “MyData provision” contribution within the PoC algorithm.
- Privacy-preserving insight extraction: Rather than directly centralizing the raw data distributed on users' mobile phones, PRODAO uses PETs such as Federated Learning and Homomorphic Encryption so that AI models extract collective insights, trends, and correlations from the data, thereby leveraging the collective value of the data while protecting individual privacy.
- Decentralized data aggregation and synthesis: Using only anonymized or zero-knowledge-proven data attributes, without sensitive information, AI generates large-scale, meaningful synthetic datasets or performs statistical aggregation to build the datasets needed for specific industries or research fields.
5.3. Personalized Services and Smart Matching
Through AI-based intelligent matching and personalization functions, PRODAO optimizes the user experience and improves the efficiency of resource allocation within the ecosystem.
The creation of AI personas provides customized services and smart matching so that PRODAO users can have experiences optimized for them. AI analyzes a user's contribution activities, content-consumption patterns, search history, collaboration history, and DID/VC-based expertise profile to build a dynamic “AI persona.” Throughout this process, user consent and privacy-enhancing technologies are applied.
Smart Matching Functions
- Expert–user/project matching: Analyzes a user's questions and project requirements to recommend the most suitable experts in the relevant field (individual members or star clusters). AI comprehensively considers the experts' expertise, reputation, activity history, and more.
- Data–AI model/consumer matching: Recommends data providers (users) who hold a specific type of data needed for AI-model training to AI-model developers or data consumers; in this process, only the data attributes are verified through zero-knowledge proofs.
- Content recommendation and curation: Based on a user's interests, learning level, and past interactions, recommends personalized professional content, news, community discussions, and more, providing the most useful information to the user amid the flood of information.
- Discovering collaboration opportunities: AI analyzes the profiles and activities of diverse experts to discover potential collaboration opportunities and recommend teams that can create synergy.
5.4. AI Model Training and Management
PRODAO applies a decentralized approach across the entire life cycle of AI models (training, deployment, management, auditing) to secure transparency and fairness.
Decentralized AI Training (Federated Learning and Incentives)
- The core training of AI models within PRODAO is conducted mainly through Federated Learning. Rather than on a central server, an AI model is trained locally on each individual user's mobile device with their own data, and only the encrypted “change in model weights” is transmitted to the server to update the final model. In this process, the raw data never leaves the user's device.
- Participation in AI-model training by providing data is rewarded in PROD tokens in accordance with the PoC algorithm, inducing active participation.
AI Model Governance and On-Chain Management
- All AI models developed, deployed, and used within the PRODAO ecosystem are version-managed on-chain and are subject to the control of DAO governance. The addition of new AI models, updates to existing models, changes to key parameters, and the like are decided through votes by PROD token holders.
- This helps prevent the deployment of malicious AI models and secures the transparency and reliability of the models.
Model Evaluation and Auditing
- Evaluation and auditing of AI models' performance, fairness, bias, security vulnerabilities, and the like are conducted in a decentralized manner. Independent audit nodes or expert groups verify a model's behavior, and the results are recorded on-chain.
- PRODAO encourages open-source AI-model development and operates a community-based code-review and vulnerability-reporting system.
AI Model Marketplace
- PRODAO provides a decentralized marketplace where verified AI models (e.g., data-analysis models for specific fields, content-generation models) can be shared and traded. Model developers receive fair compensation for their model contributions, and other participants can use the AI functions they need.
- This contributes to increasing the liquidity and utilization of AI assets within the PRODAO ecosystem.