From Code to Consent: Legal Validity of Machine-Made Declarations in International Trade
- DOI
- 10.2991/978-94-6239-705-7_5How to use a DOI?
- Keywords
- Artificial Intelligence; Cross-border Trade; UNCITRAL Model Law on Automated Contracting; MLAC
- Abstract
The use of artificial intelligence (AI) in supply chain management holds great potential for savings, as processes can be designed very efficiently without human intervention. However, the use of AI in cross-border trade requires that contractual declarations issued by the AI are legally valid. Unharmonized national law creates impediments with individual solutions for this problem. While prior instruments of the UN Commission on International Trade Law (UNCITRAL) address e-commerce, they fall short on AI’s autonomy. A new Model Law on Automated Contracting (MLAC) shall fill this gap. This study employs a doctrinal research methodology to review the MLAC regarding its regulatory content within the framework of UNCITRAL texts, drawing on primary and secondary legal sources of the UNCITRAL as well as a comprehensive literature review. Through this approach, the article identifies both strengths and limitations of the MLAC and assesses its benefits for international businesses. Finally, the paper introduces a novel practitioner-focused framework for MLAC implementation.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Kerstin Kern PY - 2026 DA - 2026/06/13 TI - From Code to Consent: Legal Validity of Machine-Made Declarations in International Trade BT - Proceedings of the FIREtalk Conference - Research on FIRE! (research-on-fire 2025) PB - Atlantis Press SP - 51 EP - 67 SN - 2352-5398 UR - https://doi.org/10.2991/978-94-6239-705-7_5 DO - 10.2991/978-94-6239-705-7_5 ID - Kern2026 ER -