AI and Formula One: Where Law Must Catch Up with Speed

Abstract

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.

Introduction

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: 

• Who is accountable when AI gets it wrong?
• Can decisions influenced by AI be appealed?
• Who owns the data and models that power performance?

I. The Current State of AI in Formula One

AI has been adopted across several domains in F1, including:

• Real-time strategy: Teams use AI to simulate thousands of race scenarios based on live telemetry, tyre degradation, and opponent behaviour².
• Driver analytics: Performance data is analysed with machine learning to optimise racing lines, throttle patterns, and brake usage.
• Aerodynamic design: McLaren, among others, uses AI-enhanced CFD simulations to reduce reliance on wind tunnel testing³.
• Race control: The FIA’s Remote Operations Centre (ROC) incorporates AI-assisted tools to help stewards monitor race incidents and potential breaches⁴.

II. Case Studies: When AI and Data Shape Results

1. The 2021 Abu Dhabi Grand Prix

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.

2. 2023 Austrian Grand Prix — AI Flags Track Limits

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.

3. McLaren and AI-Driven Car Development

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.

III. Legal Challenges at the AI Motorsport juncture

1. Liability and Fault Attribution

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?

• Comparative law: In Various Claimants v Morrisons Supermarkets [2020] UKSC 12, the Supreme Court reinforced the idea that organisations can be vicariously liable for autonomous actions linked to their systems.
• F1 Application: Teams may need to adopt AI-specific risk clauses in their contracts with vendors to delineate fault attribution.

2. Transparency and Explainability

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?

• Parallel in law: In R (Bridges) v Chief Constable of South Wales Police [2020] EWCA Civ 1058, the Court of Appeal found that facial recognition technology lacked adequate legal safeguards, partly due to its opacity.
• Implication for F1: Unless AI decisions are explainable, the FIA may face appeals on the grounds of procedural unfairness or lack of disclosure.

3. IP Ownership and Data Governance

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.

• Legal grey area: Under UK law (CDPA 1988), works generated by a computer may be attributed to the person “who made the arrangements necessary” but this is ambiguous when training and refinement are shared.
• FIA context: The FIA’s own sporting code is silent on the ownership of AI-trained strategy models, leaving room for future challenge.

IV. Formula One as a Model for AI Regulation

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.

Recommendations:

• Mandatory audit trails: Any AI system influencing race outcomes or disciplinary decisions should produce a human-readable log of its reasoning.
• Explainability clauses: The FIA and teams should require that any AI systems used in officiating be “explainable” under defined criteria.
• Liability frameworks: Teams should adopt standardised provisions allocating liability for AI-induced harm or error.
• Shared data protocols: There should be explicit terms for AI model training when data originates from FIA sensors or shared sources.

Conclusion: Keeping Law on the Grid

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.

References & Footnotes

1. FIA Press Release: "Remote Operations Centre", 2022 – https://www.fia.com 
2. Interview with Andrew Shovlin (Mercedes Strategy Director), Sky Sports F1, March 2023. 
3. McLaren Technology Partnership with Dell Technologies, 2022 — https://www.mclaren.com/racing/partners/dell
4. FIA Technical Briefing on ROC, 2022. 
5. BBC Sport, "Abu Dhabi GP: FIA admits 'human error' but says rules were followed", March 2022. 
6. See Associated Provincial Picture Houses Ltd v Wednesbury Corporation [1948] 1 KB 223. 
7. F1.com, “Austrian GP sees record lap deletions due to track limits”, July 2023. 
8. See R v Secretary of State for the Home Department, ex parte Doody [1994] 1 AC 531 (HL). 
9. Motorsport.com, "McLaren Using AI to Accelerate Development", 2023.
10. Copyright, Designs and Patents Act 1988, Section 9(3).