# The Development of an OpenAI-Based Solution for Decision-Making
## 5 Discussion and Implications
### Potential Risks and Limitations  
Despite the promising results, several challenges and limitations must be acknowledged to contextualize the system's effectiveness and guide improvements:  
* AI Bias and Fairness: AI models can inadvertently perpetuate biases present in training data or algorithms. Without careful oversight, recommendations might favor or disadvantage certain groups (e.g., along gender or ethnic lines), raising fairness concerns. In our implementation, we conducted a fairness evaluation comparing AI decisions to human decisions across demographic groups (using metrics like demographic parity and equalized odds). After bias mitigation, the AI system's acceptance rates were almost equal across protected vs. unprotected groups, indicating that fairness criteria were met (DP within \(\sim\)1%, EO within \(\sim\)2%). However, ensuring long-term AI fairness remains an ongoing risk. The model must be continuously monitored and updated with diverse, representative data to prevent drift into biased decision patterns. Any AI-driven decision support system should include bias audits and allow for human review to maintain fairness. * Security Vulnerabilities in Blockchain Integration: While blockchain adds security through cryptographic immutability and distributed consensus, it also introduces new attack surfaces. Smart contracts could contain bugs or vulnerabilities (such as re-entrancy attacks) that malicious actors might exploit. For example, a poorly written voting contract might be manipulated to falsify results or drain resources. Our use of a Proof-of-Authority network means that the system relies on a set of trusted validators; if a validator node is compromised or colludes, tampering with the decision ledger is possible. PoA consensus has known limitations, including lower resistance to censorship or insider attacks. To mitigate risks, we implemented thorough smart contract audits, used well-tested libraries for critical functions, and limited on-chain data to hashes (to reduce attack incentive). All user interactions are secured via HTTPS and token-based authentication on the backend, and sensitive data are encrypted at rest. Nonetheless, cybersecurity remains a concern, and continuous security testing and updates are necessary. Future versions should consider more decentralized consensus models or advanced permissions to further reduce the impact of any single point of failure. * Scalability Constraints: The solution's performance in a mid-sized organization was excellent, but scaling to thousands of users or decisions could pose challenges. Blockchain throughput is a bottleneck for many systems--public blockchains like Ethereum handle in the order of tens of transactions per second, which may not suffice if an organization is recording very frequent decisions or votes on the chain. While our private PoA blockchain handled the case studies with negligible latency (\(\sim\)20 s per transaction), increased workload or more complex smart contracts could introduce delays. Additionally, the AI component, if faced with substantially more simultaneous queries or more complex analysis tasks, might require more computational resources or advanced optimization. System scalability may require load balancing, off-chain vote aggregation (recording only final results on chain), or exploring layer-2 blockchain solutions to maintain low latency and cost as usage grows. Careful architectural adjustments and possibly switching to a consensus protocol with higher throughput will be needed to support large-scale deployments without sacrificing performance. * Adoption and Organizational Challenges: Introducing an AI-blockchain decision system into an existing organizational culture can be challenging. Some stakeholders might be hesitant to trust AI recommendations or fear that the technology could replace human judgment. Indeed, despite the clear benefits of AI, some managers are reluctant to embrace such technologies due to various adoption hurdles.