Topic: Digitalisation

The legal industry is on the brink of transformation with the integration of Generative AI (GenAI). Understanding which AI solutions fit your practice is crucial to enhancing productivity.

AI adoption must now be on every board room and executive team’s agenda as an emerging area of risk and regulatory focus. This issue has been given further impetus by a number of key developments across Australia’s multi-jurisdictional landscape.

This webinar will look at the legal risks to consider when developing and using AI in the UK in 2025, including considerations for generative AI tools. It will provide an update on UK AI policy and new laws proposed. It will also look at existing cross-sectoral and sectoral laws and how they apply to AI, and the application of the EU AI Act to UK businesses.

n response to the rapid development of artificial intelligence (AI) technologies, the Cyberspace Administration of China (the CAC) recently issued two draft regulations for public consultation: Measures for Labelling Artificial Intelligence-Generated or Synthetic Content (the Draft AI Labelling Measures) and Cybersecurity technology—Labelling method for content generated by artificial intelligence (the Draft Labelling Method Standard).

The privacy-cyber world seems preoccupied with issues related to the nexus between personal data and AI. Those issues, although important, are dwarfed by a more pressing and fundamental question: can we get AI to do useful things reliably and accurately in the realm of predicting significant human outcomes, such as health, criminal propensity, credit risk etc. (“Predictive AI”)? Arvind Narayanan and Sayash Kapoor, two luminaries in the AI field from Princeton University, suggest the answer is “No” and they make their case in AI Snake Oil: What Artificial Intelligence Can Do, What it Can’t and How to Tell the Difference. Although we very much recommend the book—as it is excellent—we think the thesis is too pessimistic. Companies should not “throw the baby out with the bathwater” but instead distill the precepts that will allow for development of a more rigorous predictive AI that avoids known pitfalls.