A Roadmap for Regulating Generative AI

Michael Watkins and Michael Wade

Artificial Intelligence (AI) is rapidly transforming businesses, governments, and society. From automating customer service interactions to assisting with medical diagnoses and financial trading, AI has proven its capacity to deliver immense economic and societal benefits. Yet alongside these opportunities come new risks—some of which are already materializing. Unregulated AI could lead to mass job displacement, more intrusive data practices, biased decision-making, and, in extreme scenarios, large-scale harm to critical infrastructure or public health.

Despite AI’s promise, a significant gap in regulation remains. While some jurisdictions have taken initial steps—such as the European Union’s AI Act and China’s rules for generative AI—many regions still lack sufficient guardrails. Even where frameworks exist, the rapid evolution of AI complicates enforcement. Leaders and policymakers must, therefore, develop robust, forward-looking policies.

One productive path is to consider how five other high-risk industries—nuclear power, aviation, pharmaceuticals, automotive manufacturing, and finance—have confronted similar existential risks. Although these fields differ in technology and historical context, they demonstrate how strict oversight can coexist with ongoing innovation.

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Lessons from Five High-Stakes Industries

The five industries highlighted – nuclear power, aviation, pharmaceuticals, automobile manufacturing, and finance – did not achieve tight oversight simply by chance. Specific accidents, crises, or tragedies often forced regulators and industry stakeholders to develop stronger standards.

Nuclear Power

Nuclear power’s catastrophic failures highlight the need for rigorous safety protocols and international collaboration. The Three Mile Island accident (1979) revealed how design flaws and human errors could escalate quickly. The more devastating Chernobyl disaster (1986) underscored the importance of transparency and accurate information-sharing. Fukushima Daiichi (2011) illustrated how natural disasters could trigger catastrophic chain reactions. Each incident drove tighter operator certification, fail-safe mechanisms, and global treaties—lessons vital to AI, which can fail unexpectedly if not properly safeguarded.

Aviation

Aviation has evolved through continuous scrutiny of accidents and near-misses. Crashes involving the De Havilland Comet jets in the 1950s led to better engineering standards, while the Tenerife disaster (1977) spotlighted communication and procedural failures. Over decades, agencies like the Federal Aviation Administration (FAA) and the International Civil Aviation Organization (ICAO) introduced mandatory certification, maintenance checks, and transparent reporting systems, resulting in one of the safest transportation methods today. Similarly, AI could benefit from robust testing, thorough investigations of failures, and open reporting of “near misses.”

Pharmaceuticals

Drug development shows the life-and-death stakes of insufficient oversight. The infamous thalidomide tragedy (late 1950s–early 1960s) caused severe birth defects in thousands of babies, leading to stricter clinical trial regulations. More recently, Merck withdrew Vioxx (2004) after discovering it posed serious heart-attack risks, prompting stronger post-market surveillance. By analogy, AI systems require pre-deployment trials (akin to sandboxes) and continuous monitoring, ensuring that harmful biases or unforeseen “side effects” are detected before they proliferate.

Automobile Manufacturing

The automotive industry has long grappled with product liability and design flaws. The Ford Pinto scandal in the 1970s exposed how cost-cutting could result in lethal defects, sparking widespread use of recalls and stricter standards. Modern examples—like Toyota’s unintended acceleration—underscore the risks when software and mechanical components interact in complex systems. For AI, where software often takes center stage, these lessons translate into robust testing and clear accountability frameworks.

Finance

Finance highlights the systemic risk posed by complex, automated processes. The Great Depression (1929) spurred fundamental banking regulations, while the 2008 global financial crisis revealed how unregulated instruments (e.g., mortgage-backed securities) could destabilize economies. In its aftermath, governments introduced capital requirements and stress testing to contain systemic threats. AI-driven high-frequency trading or credit-scoring algorithms can similarly amplify hidden risks unless thoroughly overseen.

These industries show that major crises typically stimulate international collaboration, continuous oversight, transparent investigations, and strict standards. We should adopt these lessons preemptively for AI rather than wait for a large-scale catastrophe to spur action.

Seven Key Elements of a Comprehensive Regulatory Framework

Like aviation or pharmaceuticals, AI sits at a crossroads of potentially enormous benefits and severe risks. Below are seven elements, derived from the five industries, that form a comprehensive regulatory framework for high-risk technologies:

1.????? Tiered Risk Classification

o??? Key Insights: Just as pharmaceuticals schedule drugs by their risk and finance applies extra scrutiny to complex products, AI systems should be categorized by their potential for harm.

o??? Application to AI: Low-risk AI, such as basic chatbots, would have fewer requirements, while high-risk systems, such as critical infrastructure management and autonomous weapons, require stringent oversight. The EU AI Act adopts this tiered approach.

?2.????? Licensing & Certification

o??? Key Insights: Nuclear plants demand specialized operator licenses; pilots and airlines undergo rigorous certification.

o??? Application to AI: Organizations deploying critical AI systems could be required to obtain licenses that verify their technical competence, data governance, and safety protocols. China’s Cyberspace Administration (CAC) rules for large language models reflect this approach.

3.????? Rigorous Testing & Validation

o??? Key Insights: Aviation relies on exhaustive flight simulations and testing; pharmaceuticals go through multi-phase clinical trials.

o??? Application to AI: Regulatory sandboxes can assess an AI’s performance on safety, fairness, and reliability metrics before widespread deployment, minimizing the risk of untested or biased models entering the market.

4.????? Continuous Monitoring & Post-Market Surveillance

o??? Key Insights: Automotive recalls, pharmaceutical post-market checks, and nuclear inspections all address latent defects that emerge post-launch.

o??? Application to AI: Ongoing audits, performance reporting, and periodic re-certifications can ensure AI doesn’t drift in unintended directions—e.g., an autonomous vehicle’s navigation software failing in new weather conditions.

5.????? Transparency & Explainability

o??? Key Insights: Finance mandates disclosures; pharmaceuticals require clear labeling.

o??? Application to AI: “Model cards” documenting training data sources, limitations, and intended use could be mandatory. For high-impact AI decisions—such as in hiring or lending—explainability helps detect and correct bias, thereby building public trust.

6.????? Incident Investigation & Remediation

o??? Key Insights: Aviation’s accident investigations (e.g., NTSB reports) are transparent and lead to broad systemic improvements.

o??? Application to AI: Creating independent investigatory bodies with subpoena power—modeled on the NTSB—would allow thorough, public examinations of AI failures, from algorithmic trading meltdowns to autonomous vehicle crashes.

7.????? Liability & Accountability

o??? Key Insights: Automotive manufacturers and financial institutions face penalties or lawsuits for harmful products.

o??? Application to AI: Clear liability across developers, data providers, and end-users discourages corner-cutting. Knowing they can be held directly responsible pushes stakeholders to adopt safer practices.

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The Unique Challenges of Regulating AI

While these elements provide a starting point for build a framework, AI also introduces unique hurdles that regulators must address:

Adaptive and Evolving Systems Many AI models continually learn post-deployment, changing in unpredictable ways. Traditional regulatory regimes designed for static products need flexible mechanisms—like version tracking and re-certifications—to reflect this dynamism.

Open-Source Ecosystems AI solutions often combine open-source code with proprietary components. Assigning liability is difficult when many contributors are spread across different jurisdictions. Policymakers must define how responsibility is allocated among developers, data providers, platform hosts, and users.

Black Box Complexity and Explainability Gaps Deep neural networks can be opaque. Perfect transparency may be unattainable, but regulators should insist on pragmatic explainability—enough to spot bias, safety hazards, or blatant misuse.

Speed of Deployment AI can propagate worldwide in seconds. A new model can be cloned, modified, and released across multiple regions almost instantly, challenging any single government’s enforcement. Cross-border collaboration is thus imperative to avoid “forum shopping” for lax regulations.

Enforcement Challenges Unlike tangible goods, AI code can be copied, altered, and distributed with ease. Traditional inspection methods (like testing chemical compositions) won’t apply. Regulators must invest in specialized algorithmic audit tools and processes designed specifically for digital ecosystems.

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A Regulatory Roadmap for AI

Bringing these elements together demands careful collaboration among businesses, regulators, and civil society. Below are the key elements of a comprehensive roadmap:

1. ?Establish a Tiered Risk Framework Action: Classify AI applications by potential harm. Benefit: Focus resources on the most dangerous use-cases. Risk of Omission: Overlooking critical systems that might fail catastrophically if misclassified.
2. Define Licensing & Certification Pathways Action: Require specialized licenses for top-tier AI use-cases. Benefit: Professionalizes AI deployment, akin to pilot or nuclear plant certifications. Risk of Omission: Unqualified entities could develop lethal or unsafe AI tools.
3. Implement Rigorous Testing & Validation Protocols Action: Mandate multi-phase testing (sandboxes) and standardized benchmarks. Benefit: Prevents the release of dangerously biased or untested systems. Risk of Omission: AI that fails under certain real-world conditions (e.g., weather extremes) could cause fatal accidents or market crashes.
4. Deploy Continuous Monitoring & Post-Market Surveillance Action: Enforce performance audits, logs, and re-certifications. Benefit: Detects model drift and emergent risks before they spiral out of control. Risk of Omission: AI systems degrade or develop unforeseen flaws that remain unaddressed.
5. Mandate Transparency & Explainability Action: Require “model cards” and feasible explainability for high-stakes decisions. Benefit: Builds public trust, enables targeted oversight. Risk of Omission: Hidden biases or discriminatory outcomes remain undetected.
6. Establish Incident Investigation & Remediation Processes Action: Create independent boards to investigate major AI failures. Benefit: Encourages open learning from mistakes, as in aviation. Risk of Omission: Persistent, systemic problems remain hidden if investigations are secret or nonexistent.
7. Clarify Liability & Accountability Action: Define fault lines across the AI supply chain. Benefit: Discourages risky shortcuts; victims have legal recourse. Risk of Omission: Ambiguity on who pays the cost when AI causes harm fosters irresponsible behavior.

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Striking the Right Balance

Potential “trigger events,” such as a catastrophic energy infrastructure failure or a deepfake-driven political crisis, could accelerate regulation if they produce public outcry. However, a proactive approach, informed by lessons from other high-stakes sectors, is preferable to waiting for disaster.

The history of nuclear power, aviation, pharmaceuticals, automobile manufacturing, and finance shows that robust regulation can co-exist with innovation, provided the rules are well-designed and rigorously enforced. Because AI technology is fast-moving, globally distributed, and often opaque, its regulation requires novel tools and international cooperation.

Businesses should view compliance as a legal requirement and a competitive advantage in building consumer trust. Policymakers, in turn, must engage technologists and ethicists to draft agile regulations that adapt as AI evolves. We can ensure that AI's transformative potential is harnessed safely and responsibly by implementing tiered risk assessments, licensing, rigorous testing, continuous monitoring, transparency mandates, investigative processes, and unambiguous liability rules.