Formula One (F1) has entered a new era, not driven by horsepower or downforce, but by data and algorithms. Artificial intelligence (AI) is now central to team strategy, car development, and even race governance. Yet, as AI gains influence, the legal framework underpinning it remains underdeveloped, particularly in high-stakes, fast-moving environments like motorsport. This article explores the legal and ethical implications of AI in F1 through real-world examples and proposes a path for regulation that balances innovation with accountability.
Formula One has long been a crucible for technological advancement. Many innovations we take for granted in consumer vehicles, from disc brakes to energy recovery systems, began on the F1 grid. Today, F1 is embracing AI at pace, using machine learning for everything from race strategy to officiating decisions1.
But innovation without governance invites uncertainty. As AI begins to shape race outcomes, team decisions, and rule enforcement, fundamental legal questions arise:
AI has been adopted across several domains in F1, including:
In the controversial finale of the 2021 season, Race Director Michael Masi altered the application of the safety car procedure in a decision that arguably handed the title to Max Verstappen5. Though no AI was involved, the incident highlighted the need for consistency, transparency, and technical support in decision-making. The FIA subsequently introduced AI-assisted video tools and a more structured stewarding framework.
Legal note: The issue triggered debate on the limits of discretionary authority in rule interpretation. Under administrative law principles, even discretion must be exercised reasonably and fairly6.
At the Red Bull Ring, AI-assisted systems were used to detect track limit breaches. The system flagged over 1,200 violations, leading to more than 100 deleted lap times7. Teams protested that the system’s criteria were unclear and overly rigid.
Legal note: This raised questions about automated enforcement and due process. In UK law, procedural fairness includes the right to understand and challenge decisions affecting one's rights8.
McLaren’s partnership with Dell and Arrow Electronics produced AI-powered simulations that significantly reduced wind tunnel time. However, internal questions reportedly emerged around ownership of AI-generated models and data sovereignty9.
Legal note: This touches on the intellectual property status of machine-generated content. UK IP law does not clearly define ownership rights for works generated by non-human agents10.
If an AI system provides erroneous advice that leads to a crash or poor performance, who is responsible? The engineer who acted on the advice? The software vendor? The AI model itself?
The use of AI in officiating (e.g. flagging track limit breaches or collisions) must comply with natural justice principles. If an AI tool generates a decision that leads to a sanction, can that decision be challenged or understood?
Data from sensors, AI models, and simulations forms a core part of team IP. But many of these tools are developed in collaboration with third parties, raising questions about ownership and exploitation rights.
F1’s unique governance structure, with its mix of private agreements, contractual norms, and a single international regulator (the FIA) provides a controlled environment in which AI regulation can evolve ahead of broader legal systems.
As AI accelerates across the motorsport industry, legal frameworks are being tested in new and unprecedented ways. The issues F1 faces today, around liability, fairness, and IP, are the same ones confronting industries from aviation to healthcare.
Formula One is no stranger to pioneering change. Now it has a chance to do so again, not just on the racetrack, but in setting a global example for how law can keep pace with innovation. As a barrister, I argue not for caution, but for constructive legal foresight. ensuring AI in sport upholds the same values that underpin the rule of law: fairness, accountability, and transparency.