A transfer order made by the US judicial panel on multidistrict litigation on Thursday said that centralisation will "allow a single judge to coordinate discovery, streamline pretrial proceedings, and eliminate inconsistent rulings". Cases brought in California by prominent authors including Ta-Nehisi Coates, Michael Chabon, Junot Díaz and the comedian Sarah Silverman will be transferred to New York and joined with cases brought by news outlets, including the New York Times, and other authors including John Grisham, George Saunders, Jonathan Franzen and Jodi Picoult. Most of the plaintiffs opposed consolidation, arguing that their cases were too different to be combined. OpenAI had proposed consolidating the cases in northern California. The judicial panel ultimately transferred the cases to the southern district of New York, stating that centralisation would "serve the convenience of the parties and witnesses" and "promote the just and efficient conduct of this litigation".

Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.

Artificial intelligence is already changing the way we live our daily lives and interact with machines. From optimising supply chains to chatting with Amazon Alexa, artificial intelligence already has a profound impact on our society and economy. Over the coming years, that impact will only grow as the capabilities and applications of AI continue to expand. AI promises to make our lives easier and more connected than ever. However, there are serious ethical considerations to any technology that affects society so profoundly.

There will, of course, be many problems to be solved before these tasks can be regarded as satisfactorily completed, and before we can speak with confidence out of experience. But these problems do not appear to have any fundamentally insuperable content. The difficul-- ties are manmade rather than intrinsic. They originate in part from the difficulty of adjusting the organisms of office life to new rhythms, new environments, new relationships, in part from imperfect understanding and appreciation of the power and range of new techniques, and in part from a lack of perception of the limitations and deficiencies of these systems. We may reasonably suppose that, during the course of the next five years, these difficulties will be overcome and that, throughout Government Departments and Industry, there will be a growing number of installations at work on these jobs. With this perhaps over--simplified premise, it is not too early to start thinking about a possible future form of A.D.P. in Government Departments in, say, ten or fifteen years' time.

The paper examines the extent to which a less mechanistic approach may be possible and suggests limitations that may be imposed not only by human limitations but by difficulties of correspondence and significance between machine and manual situations. 1. INTRODUCTION LET us assume that automatic data processing (A.D.P.) can do the things that we are planning for it to do at present, such as payroll, stores accounting, and statistical analyses. There will, of course, be many problems to be solved before these tasks can be regarded as satisfactorily completed, and before we can speak with confidence out of experience. But these problems do not appear to have any fundamentally insuperable content. The difficul-- ties are manmade rather than intrinsic. They originate in part from the difficulty of adjusting the organisms of office life to new rhythms, new environments, new relationships, in part from imperfect understanding and appreciation of the power and range of new techniques, and in part from a lack of perception of the limitations and deficiencies of these systems. We may reasonably suppose that, during the course of the next five years, these difficulties will be overcome and that, throughout Government Departments and Industry, there will be a growing number of installations at work on these jobs. With this perhaps over--simplified premise, it is not too early to start thinking about a possible future form of A.D.P. in Government Departments in, say, ten or fifteen years' time.