Jus Mundi Opens Arbitrator Search Beta

Jus Mundi announced on 27 October 2026 that its private beta of arbitrator search in Jus AI will help arbitration practitioners identify candidates against case criteria and research named candidates before nomination, acceptance or objection. The company says the capabilities are designed to support professional judgment with cited evidence, visible information gaps and safeguards. The sponsored article is by Jean-Rémi de Maistre, Jus Mundi’s CEO.

Selecting arbitrators can shape international disputes in which billions of dollars may be at stake. Parties, counsel and institutions weigh candidates’ professional backgrounds, expertise across relevant issues and jurisdictions, language skills and potential conflicts. For a three-member tribunal, they also consider how prospective members may work together. The process draws on quantitative data, qualitative insights and feedback, and overlooking information can expose an appointment to challenge, disrupt proceedings and increase costs.

The article says selection still relies heavily on manual research, internal know-how and word of mouth, while relevant records are scattered across profiles, case records, publications and other sources. Jus Mundi’s analysis of major arbitral institutions’ 2024-2025 statistics found that women accounted for 35%-46% of direct institutional appointments and 17-24% of party nominations. The company argues that AI could help practitioners compare candidates and look beyond familiar networks, while stressing that suitability remains a judgment call.

The article identifies three challenges for legal AI used in selection: building a reliable evidence base that links the right sources to the right practitioner; assessing evidence without treating a previous appointment as proof of suitability or attributing a tribunal’s collective reasoning to one arbitrator; and addressing ethical risks. These include inaccurate claims about a practitioner, uneven documentation, algorithmic bias that may reproduce past appointment patterns, discriminatory uses and mishandling personal or professional information. It says systems should explain recommendations and link evidence to sources, show uncertainty and gaps, and apply relevant criteria consistently without reducing candidates to a falsely precise score.

Jus Mundi says its beta combines Jus Connect, which has thousands of data-backed professional profiles, arbitration materials and users’ own documents through My Sources. Jus AI uses Tenet v5, the company’s proprietary large language model built for arbitration, an agentic architecture and lawyer-led quality assurance. Answers include citations, flag information gaps and partial coverage, and follow guardrails that define what the system answers, qualifies or declines. The company plans to invite members of its Mission Committee, clients, partners and organizations focused on diversity and professional standards to test the beta and provide feedback. Future development is focused on deeper analytics and more integrated conflict-checking workflows.

Jus Mundi announced on 27 October 2026 that its private beta of arbitrator search in Jus AI will help arbitration practitioners identify candidates against case criteria and research named candidates before nomination, acceptance or objection. The company says the capabilities are designed to support professional judgment with cited evidence, visible information gaps and safeguards. The sponsored article is by Jean-Rémi de Maistre, Jus Mundi’s CEO.

Selecting arbitrators can shape international disputes in which billions of dollars may be at stake. Parties, counsel and institutions weigh candidates’ professional backgrounds, expertise across relevant issues and jurisdictions, language skills and potential conflicts. For a three-member tribunal, they also consider how prospective members may work together. The process draws on quantitative data, qualitative insights and feedback, and overlooking information can expose an appointment to challenge, disrupt proceedings and increase costs.

The article says selection still relies heavily on manual research, internal know-how and word of mouth, while relevant records are scattered across profiles, case records, publications and other sources. Jus Mundi’s analysis of major arbitral institutions’ 2024-2025 statistics found that women accounted for 35%-46% of direct institutional appointments and 17-24% of party nominations. The company argues that AI could help practitioners compare candidates and look beyond familiar networks, while stressing that suitability remains a judgment call.

The article identifies three challenges for legal AI used in selection: building a reliable evidence base that links the right sources to the right practitioner; assessing evidence without treating a previous appointment as proof of suitability or attributing a tribunal’s collective reasoning to one arbitrator; and addressing ethical risks. These include inaccurate claims about a practitioner, uneven documentation, algorithmic bias that may reproduce past appointment patterns, discriminatory uses and mishandling personal or professional information. It says systems should explain recommendations and link evidence to sources, show uncertainty and gaps, and apply relevant criteria consistently without reducing candidates to a falsely precise score.

Jus Mundi says its beta combines Jus Connect, which has thousands of data-backed professional profiles, arbitration materials and users’ own documents through My Sources. Jus AI uses Tenet v5, the company’s proprietary large language model built for arbitration, an agentic architecture and lawyer-led quality assurance. Answers include citations, flag information gaps and partial coverage, and follow guardrails that define what the system answers, qualifies or declines. The company plans to invite members of its Mission Committee, clients, partners and organizations focused on diversity and professional standards to test the beta and provide feedback. Future development is focused on deeper analytics and more integrated conflict-checking workflows.