Responsible Generative AI: Global Regulatory Gaps, US Innovation Catalysts

Abstract

This research examines how the current lack of strict regulations for generative AI affects productivity and innovation in the US IT services industry. The study uses a mixed-methods approach. It provides a comparative policy analysis of the US, the UK, and China. A survey of industry practitioners was conducted to evaluate perceived risks and factors driving adoption. The findings show a significant global difference in regulatory philosophies. It ranges from the US's market-driven voluntarism to the UK’s principles-based sectoral model. China has a state-controlled vertical model. The analysis highlights a widespread "risk assurance gap," in which existing US frameworks and regulations are deemed insufficient to address key issues. Some key concerns center on data protection and ethical governance. A surprising discovery is that these regulatory gaps are not barriers but actually help in the adoption of generative AI. The lack of strict rules, especially regarding transparency and accountability, is fostering a "first-mover advantage" mentality amongst the IT service companies. It is accelerating the deployment of generative AI solutions across critical business, support, and innovation areas. The research also emphasizes that input data is the core source of specific risks. Some key data risks are copyright infringement, privacy breaches, bias, toxicity, and misinformation. The current governance or regulations do not fully address. IT service companies seem to accept that the opportunities created by the regulatory void outweigh the future liability or compliance risks. The study concludes that the current regulatory gaps are encouraging rapid innovation and productivity while also building a risky bubble for the future. The findings point to an urgent need for targeted, risk-based regulation to clarify rules and prevent systemic risks from becoming entrenched. For IT service companies, developing strong internal AI governance is crucial for their long-term resilience.
Key Words: Generative AI Regulation, Responsible Generative AI, AI Regulatory Gap, NIST Risk Management Framework, Generative AI Data Risk