Regulation or Risk: The Global Race to Define AI’s Future

The former U.S. Securities and Exchange Commission (SEC) commissioner and current CEO of an artificial intelligence (AI) company recently emphasized the need for careful planning and execution in building an AI-enabled future. While expressing optimism about the transformative potential of AI, the executive underscored that the development and deployment of AI systems must be supported by robust frameworks to ensure safety, transparency, and accountability. This aligns with broader regulatory efforts in the U.S. and Europe, where governments are seeking to balance innovation with ethical and legal safeguards.

Recent developments in AI regulation in the U.S. illustrate a growing consensus across political lines on certain core principles. Both the Biden and Trump administrations have issued executive directives that establish baseline requirements for the use of high-impact AI systems by federal agencies. These directives emphasize the need for systematic governance, transparency, and human oversight in AI deployment. For instance, the OMB memos from both administrations require federal agencies to identify and inventory AI use cases, particularly those affecting rights or safety, and to implement minimum practices such as impact assessments and human review mechanisms before deploying AI systems [2]. These efforts reflect a shared recognition that AI must be used responsibly, especially in high-stakes domains like healthcare, employment, and critical infrastructure.

However, notable differences remain between the administrations on key issues such as equity and individual recourse. The Biden administration's approach has included proactive measures to mitigate algorithmic bias and support equity, whereas the Trump administration’s revised guidelines limit such protections to unlawful discrimination as defined by existing laws. The Trump administration has also removed requirements for individuals to be notified when AI systems impact their decisions, a shift that critics argue reduces transparency and individual rights [2]. These divergent priorities highlight the ongoing tension between risk-based and rights-based regulatory models in AI governance.

In parallel, the European Union (EU) has taken a firm stance in shaping global AI regulation, with the implementation of the AI Act, the first comprehensive AI legislation in the world. The AI Act imposes strict guardrails on high-risk AI applications, including bans on mass surveillance technologies and requirements for transparency and accountability in automated decision-making processes. This regulatory approach has drawn both support and resistance from U.S. technology firms and policymakers. While companies like Google, Microsoft, and OpenAI have engaged with the EU framework to align their AI practices with European standards, others, such as Meta, have criticized the AI Act as overly restrictive [3]. The EU’s regulatory influence, often referred to as the "Brussels Effect," has the potential to set global norms, compelling U.S. firms operating in European markets to comply with stringent data and AI governance standards regardless of domestic policy shifts [3].

Despite the Trump administration’s push for deregulation to foster U.S. competitiveness in AI, international regulatory trends suggest that strict safeguards will become increasingly normative. The global landscape is witnessing a convergence toward comprehensive AI governance, with countries like South Korea, Canada, and Australia developing their own regulatory frameworks inspired by the EU model [3]. This trend challenges the notion that deregulation alone can ensure global AI dominance, as market access in major economies will increasingly depend on compliance with international standards.

For organizations seeking to navigate this complex regulatory environment, AI-powered tools are emerging as essential for compliance monitoring. Modern AI systems can automate the detection of non-compliant behavior across communication channels, flagging unauthorized promises, data handling issues, and policy violations in real time. These tools also offer predictive capabilities, enabling organizations to anticipate and mitigate risks before they escalate. However, the effectiveness of these solutions depends on integration with existing workflows, transparency in decision-making, and secure data handling practices [4].

As AI continues to redefine industries and governance, the importance of strategic planning, ethical oversight, and regulatory alignment will only grow. The balance between innovation and responsibility will determine not only the success of individual companies but also the global trajectory of AI development.

Source:

[1] AI FAQ Series | AI Regulation: Are There Regulations on AI? (https://www.orrick.com/en/Insights/2025/08/AI-Regulation-Are-There-Regulations-on-AI-AI-FAQ-Series)

[2] 5 points of bipartisan agreement on how to regulate AI (https://www.brookings.edu/articles/five-points-of-bipartisan-agreement-on-how-to-regulate-ai/)

[3] Trump Administration AI Policy Being Stopped by the EU (https://insidetelecom.com/trump-administration-ai-policy-being-stopped-by-the-eu/)

[4] AI for compliance monitoring: A practical guide to staying... (https://www.eesel.ai/blog/ai-for-compliance-monitoring)