Introduction: From Research Lab to Criminal Law
Artificial intelligence development is typically associated with innovation, research and economic growth. Yet as models become more powerful and widely accessible, concerns have shifted from simple misuse to the development of inherently harmful or dangerously unsafe AI systems. This raises a provocative question:
When, if ever, should the development of certain AI systems be treated as a crime in itself – even before actual harm occurs?
Some argue that criminal law must step in early to prevent catastrophic risks, such as autonomous weapons, mass manipulation tools or systems that can enable large-scale cyberattacks. Others warn that criminalizing AI development could chill legitimate research, drive activity underground and be weaponized against dissidents or open-source communities.
This article explores the idea of criminalizing harmful AI development, examining what “harmful” might mean, how far criminal law should go, and where the line should be drawn between regulated risk and punishable wrongdoing.
1. What Does “Harmful AI Development” Mean?
“Harmful AI development” is not a term of art in criminal law; it covers a spectrum of activities:
- Explicitly malicious development
- Designing AI systems with the primary aim of committing crime or causing harm, e.g. generating malware, conducting cyberattacks, automating fraud or producing targeted disinformation.
- Development of inherently dangerous capabilities
- Creating or significantly advancing systems that can realistically cause massive harm if misused:
- Autonomous weapon systems,
- Models that can design biological or chemical weapons,
- Tools that radically lower barriers for sophisticated cyber intrusion.
- Creating or significantly advancing systems that can realistically cause massive harm if misused:
- Recklessly unsafe development of high-risk systems
- Building and deploying AI in critical domains (healthcare, infrastructure, finance, justice) without basic safety measures, testing or oversight, despite foreseeable risks.
- Deliberate violation of binding safety rules
- Ignoring legal obligations concerning data protection, safety standards, transparency or oversight in a way that significantly increases the risk of serious harm.
Not all of these should automatically be criminal. The crucial task is to distinguish between risky but legitimate research, regulatory non-compliance and conduct so dangerous that it warrants criminalization.
2. Why Bring Criminal Law into AI Development?
Criminal law is traditionally ultima ratio – the last resort of the legal system. So why consider it for AI development at all?
Several arguments are advanced:
- Magnitude of potential harm
- Certain AI capabilities (e.g. in cyberwarfare, biological design, autonomous weapons) might enable harm on a scale comparable to weapons of mass destruction. For such scenarios, purely administrative fines may be inadequate.
- Deterrence and signalling
- Criminalization sends a strong normative message: some lines must not be crossed, even in pursuit of innovation or profit.
- Gaps in misuse-based liability
- If law only punishes misuse after the fact, it may be too late for catastrophic scenarios; criminalizing certain forms of development aims to prevent especially dangerous capabilities from ever materialising.
However, these reasons must be balanced against the risks of overcriminalization, vagueness and abuse. Criminal law does not merely regulate; it stigmatizes and empowers the state to use its most coercive tools.
3. Categories of AI Development That Might Justify Criminalization
If criminal law is to intervene, it should do so narrowly and clearly. Three candidate categories can be discussed:
3.1. Intentional Development of AI for Criminal Purposes
Here the mental element (mens rea) is clear:
- Designing AI systems with the intent to commit crimes such as fraud, extortion, cyberattacks, deepfake blackmail or mass disinformation campaigns.
- Providing customized AI tools to criminal organisations, knowing that they will be used for illegal purposes.
In such cases, AI is analogous to building tools for burglary, hacking or terrorism. Most legal systems already criminalize similar conduct (e.g. producing malware, supplying tools for crime). Extending these to AI-specific tools is conceptually straightforward.
3.2. Development of Prohibited AI Systems (Red-Line Technologies)
Certain AI systems may be deemed intrinsically incompatible with fundamental rights or international peace – for example:
- Fully autonomous lethal weapon systems without meaningful human control,
- AI systems designed to impose total surveillance and social scoring on a population,
- Tools that enable the design and deployment of biological or chemical weapons.
Here criminal law could prohibit not only deployment but also development, testing, and transfer of such systems, akin to how some international regimes treat nuclear, biological and chemical weapons.
3.3. Grossly Reckless Development in High-Risk Contexts
A more controversial category is reckless development:
- Working on high-risk AI systems (e.g. in healthcare, aviation, nuclear facilities)
- While knowingly ignoring basic safety standards,
- In a way that makes serious harm highly likely, even if not intended.
This resembles criminal liability for gross negligence in other industries (e.g. building unsafe infrastructure). However, drawing the line between negligent research and criminal recklessness requires careful drafting to avoid punishing ordinary error or failed experiments.
4. The Risks of Overcriminalization
While some cases may justify criminalization, broad offences like “developing unsafe AI” could be dangerous:
- Vagueness and legal uncertainty
- Developers may not know ex ante what is considered “harmful” or “unsafe”, leading to self-censorship and legal risk aversion.
- Chilling effect on legitimate research
- Researchers, especially in academia or open-source communities, may avoid exploring critical safety topics or publishing negative results for fear of liability.
- Selective enforcement and abuse
- Vague offences can be weaponized against politically disfavoured actors, whistleblowers or rival companies.
- Driving development underground
- Overly strict criminalization may push risky development into jurisdictions or settings with no oversight at all, making global risk management harder, not easier.
Criminal law must therefore be precise, targeted and proportionate, with clear mental and material elements.
5. Elements of a Well-Designed Offence
If legislators decide to criminalize certain forms of harmful AI development, some design principles should guide them:
- Clear definition of prohibited conduct
- Focus on specific acts (e.g. building, training, distributing a model with defined capabilities) rather than vague notions of “dangerous AI”.
- High threshold of harm or risk
- Restrict criminalization to conduct that creates a substantial risk of serious harm (e.g. mass casualties, severe rights violations, critical infrastructure collapse).
- Robust mental element
- Require intent or at least conscious disregard of a substantial and unjustifiable risk (recklessness), not mere negligence.
- Limited scope of actors
- Target those who have effective control and decision power: lead developers, corporate directors, high-level managers – not low-level employees following instructions.
- Defences and safe harbours
- Provide defences for good-faith research conducted within recognised ethical and safety frameworks, and for disclosure of vulnerabilities for security purposes.
- Corporate liability alongside individual liability
- Ensure that corporations benefiting from reckless development can be held responsible, not just individual engineers.
6. Open Source, Dual Use and the Attribution Problem
Criminalizing harmful AI development becomes particularly complex in open-source and dual-use contexts:
- Open-source models
- Developers may release general-purpose models that can be used for both beneficial and harmful ends.
- Should they be held responsible if others later fine-tune or misuse the models for criminal purposes?
- Dual-use research
- Work on powerful models may be necessary to understand and mitigate risks, even if those models could, in principle, be misused.
- Attribution chains
- Determining which developer, team or organisation is responsible for a harmful capability can be difficult in large, distributed projects.
Here, criminal law should be particularly cautious. Blanket liability for open-source publishing or dual-use research could backfire. A more nuanced approach is to:
- Focus on intent and knowledge (did the developer know of, promote or collaborate in harmful use?),
- Combine criminal law with regulatory frameworks that impose ex ante duties (e.g. safety evaluations, access controls) without automatically criminalizing research.
7. International Dimension: Fragmentation vs. Coordination
Harmful AI development is unlikely to respect borders. If one jurisdiction criminalizes certain activities but others do not, developers may relocate or use offshore infrastructure.
This raises two issues:
- Regulatory competition and forum shopping
- Overly strict regimes may lose talent and firms to more permissive jurisdictions.
- Overly lax regimes may become hubs for risky development, exporting externalities to the world.
- Need for international coordination
- For genuinely catastrophic risks (e.g. autonomous weapons, bio-weapon design tools), international agreements or at least coordinated norms may be necessary.
Criminalization, if pursued, should ideally be aligned with international standards to avoid simply shifting risk geographically.
8. Where Should the Line Be Drawn?
Given these tensions, a reasonable line-drawing might look like this:
- Clearly criminal
- Intentional development and provision of AI tools for criminal enterprises (fraud, cybercrime, extortion, terrorist acts).
- Development and deployment of AI systems that are explicitly banned by international or national law (e.g. certain lethal autonomous weapons, population-wide social scoring).
- Regulatory, not criminal (by default)
- Most high-risk AI development, where risks can be managed through licensing, impact assessments, standards, audits and administrative sanctions.
- Violations of safety duties should generally first trigger regulatory and civil consequences, with criminal law reserved for egregious, repeated or intentionally deceptive conduct.
- Protected and encouraged, under safeguards
- Good-faith safety and security research, including dual-use models developed in controlled environments.
- Open discussion of vulnerabilities and publication of research that helps society understand and mitigate AI risks.
In short: criminal law should be a scalpel, not a sledgehammer. It should carve out a narrow zone of clearly intolerable conduct, while leaving room for regulation, civil liability and ethics to govern the vast majority of AI development.
Conclusion: Managing Risk Without Killing Responsibility or Research
The debate on criminalizing harmful AI development sits at the intersection of innovation, safety and justice. On the one hand, ignoring the possibility of ex ante criminal liability for especially dangerous AI development would underplay the unprecedented scale of potential harm. On the other hand, sweeping, vague offences risk stifling legitimate research, undermining open science and empowering arbitrary enforcement.
A balanced approach would:
- Criminalize intentional and egregiously reckless development of clearly defined, high-harm AI systems,
- Anchor most risk management in regulation, oversight and civil liability,
- Protect good-faith research and open technical discussion,
- And seek international coordination for the most extreme risk scenarios.
Ultimately, the question is not whether AI development should be free of all constraint, nor whether it should be treated as inherently suspect. The real challenge is to design a legal framework in which responsible innovation can flourish, while those who deliberately push AI towards catastrophic harm know that they are not just breaking rules – they are committing crimes.
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