Charles Redmond

Fountain Court Chambers

Charles Redmond is a barrister at Fountain Court Chambers (call 2022) with a broad commercial litigation and arbitration practice. He specialises in commercial crime, civil fraud, sanctions, insurance, and banking disputes.

Charles’s recent work includes acting (led by Clare Sibson KC) for a Lloyd’s broker charged under section 7 of the Bribery Act 2010 in SFO v United Insurance Brokers Ltd, which, if it proceeds to trial, will be the first SFO prosecution under that section to be heard by a jury. He acted in the JP Morgan v WeRealize.com proceedings in the Commercial Court and Court of Appeal, and in Karonis v JP Morgan, successfully obtaining novel anti-suit relief restraining proceedings in Greece.

Charles has experience in LCIA arbitration, offshore disputes in the Cayman Islands, BVI, and DIFC Courts, and advising on sanctions and export control matters. He is a contributing author to Montgomery and Ormerod on Fraud.

Practice Area

Corporate Crime

Panel

Contributing Author

Qualifications

BCL (2021)
BA (2020)

Education

University of Oxford (2021)

1 Contributions by Charles Redmond

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

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