Artificial Intelligence (AI) is no longer the stuff of science fiction—it’s here, revolutionising industries from transportation to healthcare. With this immense power come profound risks that we must urgently address. When AI makes a mistake, who takes the fall? This isn’t a question for the future—it’s a challenge we face today, with real-world consequences for lives, livelihoods, and the public’s trust in technology.
The integration of AI into daily life is already reshaping societal norms and creating unprecedented opportunities, from more efficient transportation to breakthroughs in medical diagnostics. However, these benefits come with equally significant risks. Consider a self-driving car making a split-second decision that results in a fatal crash. Or imagine an AI-powered diagnostic tool failing to detect a life-threatening illness, leading to devastating consequences for a patient and their family. These scenarios are not mere hypotheticals; they are happening now, and the legal system must adapt quickly to keep pace. The stakes are immense, affecting not just individual outcomes but the very fabric of societal trust in emerging technologies.
AI systems are becoming increasingly advanced, raising new and complex questions for the legal system. Who should be held accountable when an autonomous vehicle causes an accident? What happens when an AI medical tool provides an incorrect diagnosis? Can AI itself be held liable for its actions, or must we limit our focus to the humans and organisations behind it? These questions go to the heart of our relationship with technology and whether we can trust systems that often operate beyond human comprehension. As a lawyer deeply engaged with the intersection of law and technology, I am convinced that these are the conversations we need to be having now, not later. Addressing these challenges demands foresight, collaboration across disciplines, and a willingness to challenge established legal frameworks.
To fully grasp the scope of these issues, consider not only the direct consequences of AI failures but also the ripple effects they have across society. For example, each incident involving an autonomous vehicle undermines public confidence in self-driving technology, potentially delaying its adoption and its associated benefits, such as reduced traffic fatalities and lower emissions. Similarly, mistrust in AI diagnostic tools can deter their use, even when they could improve medical outcomes overall. These cascading effects demonstrate why it is imperative to create robust legal frameworks that promote accountability while fostering innovation.
The Current Legal Landscape
Right now, AI isn’t a legal “person.” Unlike corporations, which are considered legal entities and can be sued, AI systems are classified as tools or products. This means responsibility for their actions typically falls on the humans or organisations behind them—developers, manufacturers, or users. However, this model assumes human control at every level, an assumption increasingly challenged by the sophistication of AI.
For example:
- Self-Driving Cars: If an autonomous vehicle causes a crash, liability could fall on the manufacturer, the software developer, or even the car owner, depending on the specifics of the case. Legal disputes in this area are already playing out, often revealing gaps in current laws. In the UK, the Automated and Electric Vehicles Act 2018 assigns liability to insurers in cases involving automated vehicles, but this is only a starting point and does not cover all potential complexities.
- Healthcare AI: If an AI misdiagnoses a patient, the hospital or medical professional using the tool could be held accountable. This creates a chilling effect, where practitioners may hesitate to use AI tools for fear of litigation, even if those tools could improve overall outcomes. The UK’s National Health Service (NHS) has begun deploying AI tools, but questions remain about liability when these systems fail.
While this framework works for now, it assumes human oversight at every decision point. As AI grows more autonomous, this approach may no longer be sufficient. What happens when AI systems make decisions no human fully understands? In such scenarios, assigning liability becomes a challenge of untangling technological complexity from human intent.
The Challenge of Autonomy
One of the biggest hurdles in assigning AI accountability is “black-box” decision-making. Advanced AI systems, particularly those using machine learning, operate in ways even their creators struggle to explain. This raises critical legal questions:
- How do you prove negligence or intent when the decision-making process is opaque?
- Who should bear responsibility when no one fully understands why the AI acted the way it did?
Imagine a self-driving car swerves unexpectedly, causing an accident. If engineers can’t determine what prompted the manoeuvre, assigning liability becomes murky. This lack of transparency not only complicates legal proceedings but also erodes public trust in autonomous systems. For another example, consider AI systems used in hiring or lending, which have been shown to exhibit bias. If bias occurs but the process leading to the decision is untraceable, it’s unclear how to hold any party accountable or prevent recurrence.
In the UK, the General Data Protection Regulation (GDPR) includes provisions on algorithmic transparency, such as the right to an explanation for automated decisions. However, these provisions are difficult to enforce in practice, especially when AI systems rely on complex machine-learning models. Transparency is essential, but achieving it is easier said than done. Developers are under increasing pressure to make AI systems more interpretable without sacrificing their performance. However, many argue that black-box systems—which operate with superior accuracy but lack explainability—will remain indispensable in high-stakes fields. This leaves society grappling with the trade-off between functionality and accountability.
Can AI Become a Legal Entity?
Some legal scholars have proposed creating a new legal category for AI—essentially giving it a form of “limited personhood.” Under this model:
- AI could be assigned its own legal liability, with damages paid out of insurance or funds set aside by its creators.
- Developers and manufacturers would still play a role, but the AI system itself would be treated as a responsible party.
This concept is controversial. Critics argue that assigning legal personhood to AI could allow its creators to evade responsibility. If AI bears the blame, could developers sidestep their obligations entirely? Proponents counter that limited personhood would function more like a safety net, ensuring a mechanism for compensation while still holding creators accountable.
This debate parallels earlier discussions around corporate personhood, which was itself a revolutionary idea. Corporations are now integral to modern economies, and their legal personhood enables accountability while shielding individual stakeholders from excessive liability. Could a similar framework work for AI, balancing innovation with societal protections? In the UK, the ongoing discussion around AI governance in the National AI Strategy hints at a future where such radical ideas might gain traction.
The Path Forward
To address these challenges, the legal system must evolve alongside technology. Here are some steps we should consider:
- Stronger Regulations for AI Development: Governments could require AI developers to adhere to strict transparency and accountability standards, ensuring systems can be audited and understood. In the UK, this aligns with recommendations from the Centre for Data Ethics and Innovation, which advocates for greater oversight of AI systems.
- Mandatory Insurance for AI Systems: Similar to car insurance, companies deploying AI could be required to carry liability insurance to cover potential damages. This could provide a safety net for affected parties while incentivising developers to minimise risks. The UK’s Automated and Electric Vehicles Act 2018 sets a precedent for this approach, although its scope could be expanded.
- Hybrid Liability Models: Responsibility could be shared between AI creators, operators, and even the AI itself, depending on the circumstances. Such models would need to adapt to the specific characteristics of different AI applications, from consumer tools to critical infrastructure.
Why This Matters
The question of AI liability isn’t just a legal puzzle—it’s a societal one. How we assign responsibility for AI’s actions will shape how it’s developed, used, and trusted in the future. By creating clear, fair rules for AI accountability, we can ensure innovation doesn’t come at the cost of justice. In addition, robust accountability frameworks will encourage ethical AI practices, fostering public trust and enabling broader adoption of beneficial technologies.
The UK, as a global leader in AI development, has a unique opportunity to set a benchmark for responsible AI governance. By addressing the legal and ethical complexities of AI early, the UK can influence international standards while securing its position as a hub for trustworthy AI innovation.
Let’s Shape the Future Together
The intersection of AI and law represents one of the most exciting—and urgent—frontiers of our time. Whether you’re an innovator grappling with compliance or a legal professional exploring new frameworks, now is the time to shape the future of AI accountability. Let’s work together to ensure that justice and innovation go hand in hand. This is our opportunity to lead, to innovate responsibly, and to ensure that the benefits of AI are shared equitably across society.
