Global Policy Briefing
The global picture – A common challenge
The rapid growth of large language models and chatbots, most notably ChatGPT, and the increasing integration of AI-based solutions into everyday products, has put AI in the global policy and regulatory spotlight. This has driven a rush by policymakers and regulators to take action to address concerns, in the context of warnings that AI poses a threat to humanity, as well as to assess the impact AI will have on jobs and society at large.
There are many key aspects of this issue that policymakers across the globe must address, but there are also important regional differences. For example, some countries have distinct challenges when it comes to labor shortages – something AI may help to address. Others will face a significant hollowing out of jobs in particular sectors. Over the next couple of years as we see the frameworks around these issues develop, there will be, as there were over the issues of sustainable finance and data protection, an international debate over the extent to which these domestic policy frameworks need to progress with a degree of interoperability and commonality of approach. Some of the most substantive policy challenges are:
Security – The downstream risk that frontier AI models break free of human control and oversight is top of people’s minds, along with all the implications this has for everything from biosecurity and cybersecurity; to online safety and electoral risks. The international AI safety summit taking place in the UK in November will seek to build global consensus around how to tackle this.
Supply chains, skills and jobs – The Fourth Industrial Revolution will transform labor markets around the world, with some estimates suggesting that hundreds of millions of jobs could be made obsolete, while new jobs we cannot even imagine will be created. Most existing jobs will be impacted in some way. For policymakers this raises all kinds of questions, from social cohesion to reskilling and education.
Copyright and IP – The use of generative AI raises all kinds of questions for policymakers about ownership of content. What output should be credited to the creator when their work is created using tools that have been trained on output from other sources? Should it be possible to use copyrighted content to train AI tools and what should the arrangement around that be?
Public sector productivity – Against a background of a global pandemic, and geopolitical fragmentation from significant regional conflicts, the economic picture around the world is deeply challenging. One of the biggest issues for policymakers is to consider how to procure and embed AI within their public sectors to build local capability and deliver transformative outcomes from healthcare to defense, criminal justice and education.
Data protection issues – All of this intersects and builds on many of the other issues policymakers have already been grappling with in recent years looking at how to regulate personal data transfers across borders and how to balance people’s civil liberties with technological advancement.
