Legal Guidance and Research / Experts / Jacob Turner
Jacob Turner#11124

Jacob Turner

Fountain Court Chambers
Jacob Turner is a barrister at Fountain Court Chambers. He is the author of ‘Robot Rules: Regulating Artificial Intelligence’ (Springer, 2018) and a joint author of ‘The Law of Artificial Intelligence’ (Sweet & Maxwell, 2nd ed. 2024). Jacob has acted in some of the world’s most significant AI-related cases, including as counsel for Dr Thaler in the UK Supreme court in Thaler v Comptroller-General of Patents (the ‘AI Inventor’ case) and defending the first company investigated by the UK data protection regulator for alleged AI bias. Jacob is on the Attorney-General’s Panel of Counsel and has the Government on various aspects of AI regulation. He has been described in the Legal 500 directory as ‘the leading barrister in the AI space’ and is listed by Chambers and Partners as a ‘Global Leader in AI’.

In addition to his technology work, Jacob is frequently instructed in heavy commercial and sovereign matters. His clients include Venezuela, Libya, Argentina and India. Jacob was included in The Lawyer’s Hot 100 2025.

Practice Area

Panel

  • Contributing Author

Qualified Year

  • 2014

Experience

  • Cleary Gottlieb Steen & Hamilton LLP (2012 - 2017)
  • UK Supreme Court (2015 - 2016)

Qualifications

  • BA (2010)
  • MA (2013)
  • LLM (2011)

Education

  • Oxford University (2010)
  • Harvard Law School (2011)

2 Contributions by Jacob Turner

AI explainability: UK and EU legal frameworks (GDPR, DPA 2018, EU AI Act), ICO guidance, and practical steps for audits, impact assessments and transparency statements
PRACTICE NOTES
AI explainability: UK and EU legal frameworks (GDPR, DPA 2018, EU AI Act), ICO guidance, and practical steps for audits, impact assessments and transparency statements
Explainability has become a key pillar of ethical, responsible artificial intelligence (AI) and is now a common expectation within developing AI laws and rules. This Practice Note explores the explainability of AI, covering: What AI explainability means Why explainability matters Regulatory guidance on explainability The legal context for explainability Practical approaches to deliver explainability For more on AI, see Practice Notes: Artificial intelligence and machine learning—an introduction to the technology Artificial intelligence—data protection Artificial intelligence—intellectual property Artificial intelligence in the EU—the key legal issues The AI project lifecycle—a quick guide Negotiation guide—AI contracts Contractual considerations for the procurement of artificial intelligence—checklist For AI contract clauses, including issues of explainability and transparency, see: AI clauses—Warranties. For a timeline of key legal developments on AI, see Practice Notes: UK artificial intelligence—tracker and EU Artificial intelligence—tracker. What is AI explainability? The nature of artificial intelligence The conceptual groundwork for modern AI was set in the 1950s. Yet only in recent years has AI—particularly ‘machine learning’—advanced at pace...
TMT
AI in UK criminal justice: investigations to sentencing, disclosure, facial recognition, automated decision-making, admissibility, privilege, risks and reform
PRACTICE NOTES
AI in UK criminal justice: investigations to sentencing, disclosure, facial recognition, automated decision-making, admissibility, privilege, risks and reform
This Practice Note This Practice Note sets out how artificial intelligence (AI) is currently deployed within the UK criminal justice system and offers informed expectations about future uses. Mirroring the path of a criminal case—from investigation through to sentencing—it identifies and evaluates the different applications of AI along the way. It uses the working definition of AI from the government’s March 2023 White Paper, ‘A pro-innovation approach to AI regulation’, under which an AI system exhibits two key characteristics: Adaptivity: systems are trained by detecting patterns in data that humans may struggle to perceive, and are able to generate fresh inferences themselves. Autonomy: systems can reach decisions without explicit direction or ongoing human control. Technologies grounded in machine learning will typically meet this definition, as they develop in a dynamic way through experience. This definition differentiates AI from automated decision-making found in traditional logic-based systems, which rely on pre-defined rules to produce outcomes and therefore are not adaptive in the sense described...
Corporate Crime
Expert page AD
If you expected to see yourself on this page, click here.