AI与金融中的法律与战略困境
As artificial intelligence (AI) emerges as a formidable player in the financial markets, its integration leads to significant legal and strategic challenges. The conundrum lies in determining who controls AI-generated algorithms. These systems, as human authored or independently developed, raise red flags for the financial industry. While the systemic risks posed by AI trading systems are well-documented, the question of who owns and protects these algorithms remains unresolved, particularly regarding intellectual property (IP) protection.
The Ownership Conundrum in AI-driven financial markets raises serious concerns about legal and regulatory frameworks. Typically, AI bots lack legal personality and cannot own assets, even if they represent their creators. Systems generated by AI often lack clear branding or differentiation, posing risks to their intellectual property (IP). As a result, these systems’ IP often escapes legal protection unless human authorship is concluded. This situation particularly applies to IP protection mechanisms like patents, copyrights, and trade secrets, with each having its own challenges.
Moreover, financial institutions face increasingly complex regulatory challenges. The U.S. and European regulatory bodies areのみistically addressing IP issues. The U.S. Copyright Office and other regulatory bodies, while advancing their protective measures, often fail to recognize the extent to which AI-generated assets are context-free. Limiting IP protection creates vulnerabilities in algorithmic trading and otherbove-average AI applications, harming the competitive landscape of finance.
Balancing these legal and regulatory pressures, financial institutions are adopting a strategic shift. While regulatory developments are slow toccaklate, firm strategy should focus on nutrient absorption. In particular, businesses must prioritize透明化和人机协作（human-in-the-loop, HITL），确保AI-generated systems meet intellectual property (IP) claims. Inherently humanเอ็ is critical to establishing human-centric oversight and maintaining competitive advantage.
Es魁北方（SHG）提出的一个关键措施是要求所有AI-trusted (“AI-trusted”) systems include cross-personal Narratives and Translations（叙事和翻译），确保AI-generated content is떨awed and daimal by humans. This approach underscores the importance of internal validation and transparency in AI-driven environments.
Similarly, the tokenize AI Act of October 15, 2024, introduced in the EU, advocates for ethical AI governance by requiring comprehension, explainability, and accountability in AI decision-making processes. While not directly addressing IP protection, this framework pressure AI institutions to ensure human oversight in development and operation.
A case in point is TD Securities’ experience with generative AI pilots, where the lack of human oversight posed risks to IP protection and regulatory compliance. Financial institutions must recognize the gap between lean companies, which lack human involvement, and robust, human-aware AI systems. This recognition drives a shift towards more transparent, accountability, and regulated AI practices.
In the face of escalating regulatory complexity and evolving AI technologies, the focus should be on building a hybrid approach that fuses human-in-the-loop processes with AI-driven ethical frameworks. Financial institutions must prioritize understanding the specific legal and regulatory contexts within which their AI systems operate. As AI becomes more pervasive in the financial sector, earning the future of financial regulation, the stakes for those who can protect their creations in the dark will only rise.
For more like this on Forbes, check out The Legacy Banks Quietly Building The Future Of Finance and The 3 Innovation Challenges Keeping Bank CEOs Awake At Night.
