Our updated AI Toolkit, crafted by our expert technology, privacy, intellectual property, litigation, employment, competition, ESG and financial regulatory teams, is intended as a quick-reference guide for in-house counsel on all things AI.

This Toolkit starts with a technical primer and provides an overview of key AI compliance, contracting and contentious topics across the EU/UK, Asia and the US.

UK regulators have prescribed how financial institutions and market infrastructure must build their resilience to business disruption.

The EU is currently pushing through a transformational digital regulation package. However, that package is made up of a number of different laws, such as the Data Governance Act, the AI Act, NISD2, the Digital Services Act and ten other related instruments.
Keeping track of all these new laws is a challenge. Our EU Digital Regulation Handbook provides a short, accessible summary of the status of each law, together with an assessment of comparable developments in the UK. The handbook is available here.

When overseeing firms’ adoption of AI, regulators will leverage the tools within their operational resilience frameworks, including the EU’s Digital Operational Resilience Act and the corresponding rules in the UK.

During our last webinar of our series on AI in financial services in which we examined:

• The intersection of DORA and the EU AI Act
• The UK’s approach to operational resilience
• The impact of critical third party regimes

We explore the reality of implementing AI, the shifting legal landscape, AI in the Middle East and the role of AI in the energy transition, payments and online safety.

AI has captured the media zeitgeist in recent years, especially since the release of OpenAI’s ChatGPT in 2022. Countless stories probe the technology’s potential future, exploring the advancements of AI and the changes it might bring about. But what has been the real-world impact so far?

The initial euphoria surrounding generative AI has given way to a more nuanced, realistic understanding of its potential and challenges.

IBM and Red Hat’s new open-source project is designed to lower the cost of fine-tuning large language models by allowing people to collaboratively add new knowledge and skills to any model.