How could efficient judicial dispute resolution systems for B2B commercial disputes be designed in a digital world? As the artificial intelligence (AI) wave has gained momentum on an unprecedented scale in the last three years, we have recently interviewed 24 senior legal practitioners and business leaders, and we conducted an online survey which returned 275 responses from dispute resolution professionals worldwide. The majority of survey participants practice in the United States, in the United Kingdom, in Germany and in France. We argue that courts are also an indispensable part of the civil justice infrastructure for B2B commercial disputes. As far as stakeholder preferences are concerned, we find that the push for digitization and for using AI tools to improve the efficiency of judicial proceedings is strong. AI applications have already become a cornerstone of dispute resolution practice. “Online courts” should be “on offer” in commercial disputes. Providing user-friendly and reliable digital / AI tools for information management and analysis, communication and decision-making is key, as are clear protocols for online hearings. But disputing parties do not want to be judged by machines. Rather, they request competent and specialized human decision-makers. Courts need to be on top of the game with respect to the subject matter of the dispute. Parties also request planning and efficient case management. A case management conference and a process plan are essential. Finally, courts should offer an “early neutral evaluation” – a non-binding preliminary evaluation be a third party, with or without (mediated) settlement discussions – if the parties agree to this in the case management conference.
https://mediatorenausbildung.org/wp-content/uploads/2014/11/Logo-Version-.002.jpg 1000 1000 Horst Eidenmüller https://mediatorenausbildung.org/wp-content/uploads/mediationsausbildung-1030x322.jpg Horst Eidenmüller2024-01-25 13:44:472024-01-25 13:44:47Designing Efficient Judicial Dispute Resolution Systems in a Digital World