## 3 Initial Results
### Blockchain Operations That Address Ethical Issues in AI
#### 3.3.5 Token Models and Participation Incentives for Inclusiveness  
As shown in Figures 5 and 6 in the federated machine learning and data wallet operations, the main entities in the systems are organizations that control the AI model and data owners, such as victims of sexual exploitation. The data owners need to be incentivized to grant the necessary permissions for AI systems to use their data for training and retaining AI models that prevent sexual exploitation. Blockchain-based tokens provide a system for implementing a reward token for entities that actively contribute to the federated learning system. Such a token system can be well grounded on token economics that ensures that the reward system properly incentivizes both the data owners and model owners to actively participate in the federated network while addressing their separate needs. Although the token economics of incentivizing entities in federated learning and data wallet operation does not directly address ethical issues in AI systems, it can reduce bias and also improve the performance of the AI systems. By properly incentivizing data owners and victims of sexual exploitation, more individuals will share their data, hence providing a broader dataset for training the AI systems that address social problems. For organizations that host the AI models, proper incentivization ensures that they always produce models that meet the minimum performance requirements for selection into an ensemble combination of models. For instance, an incentiization rule can ensure that only organizations whose models are selected are rewarded. This ensures that the organizations will produce high-performance models, which also translates to improved performance of AI systems that address sexual exploitation. In reality, the very technical insights presented above may be easier understood through a narrative scenario: imagine a 16-year-old victim of sextortion in Romania uploading encrypted metadata of the high-risk event to a trusted app. The AI model flags suspicious coercive language patterns; the blockchain logs this action without exposing identity; a federated learning network updates detection models across jurisdictions without moving raw data; local NGOs may validate the high-risk event and initiate support. The DAO overseeing the federated platform adapts detection rules in response to emerging patterns. The CF-BIAI-SXT depicted in the following section, in line with the emerging requirements of the EU AI Act and the ALTAI framework, ensures that all these agents act in synergy to ensure that the victim is protected and the sextortion mitigation is achieved. Although ethical soundness is highly emphasized in the design phase, the actual implementation of CF-BIAI-SXT should factor in the performance overhead and storage expenses in blockchain-AI integration. For example,  
* Federated learning cost: On-chain use of smart contracts to verify performance on local models introduces latency (~200-500 ms/transaction on PoS chains like Polygon). * ZKP and encryption overheads: Zero-knowledge protocols (e.g., zk-SNARKs) result in a computational overhead of 20-40\(\times\) over regular inference pipelines.