AI Arbitration Institution Design: A Governance Disclosure Model

What governance disclosures would an AI-assisted arbitration organization need?

Researchers should look for a clear institutional identity, rulemaking authority, neutral-selection process, conflicts controls, technology responsibilities, fee structure, data practices and a correction or complaint route. The model below is a proposed transparency framework. This domain is a research publication, not an operating arbitration association.

Make identity and authority understandable

An institutional name can sound official without showing who operates it. A useful disclosure begins with the legal operator, relevant jurisdiction, governance body and source of authority to administer a dispute. It should distinguish institutional administration from the individual neutral's adjudicative role.

The reader should be able to determine whether a published policy applies by agreement, incorporation into rules or another identified mechanism. The website itself should not leave that relationship implicit.

• Operator and governance: who owns, directs and can change the process.
• Rulemaking: version history, effective dates and treatment of pending matters.
• Appointments: selection, qualifications, independence and challenge process.
• Commercial model: fees, repeat-player relationships and technology suppliers.

Explain technology responsibility in operational terms

AAA's AI Led page identifies human decision making and an AI Governance Committee in its description of oversight. Those are examples of disclosed governance features, not an endorsement or a checklist that guarantees fairness. [S1]

A hypothetical organization should explain who approves tool changes, which data the supplier receives and who investigates an incident. If a vendor can materially change a model during a proceeding, the institution should explain how it evaluates that change and communicates relevant consequences.

• Task inventory: what AI may do at each stage.
• Change control: model, configuration and procedure version.
• Incident owner: who can pause use and preserve relevant evidence.
• Party interface: where a concern is submitted and who answers it.

Synthetic disclosure card for a fictional organization

The following is a research artifact for Fictional Resolution Lab, which does not exist. It illustrates the level of specificity a reader might request, rather than asserting legal sufficiency.

• Purpose: study document organization in simulated disputes only.
• Decision maker: a named human facilitator; no binding awards in the experiment.
• Tool use: extracts document titles and dates from a synthetic record.
• Supplier access: test dataset only; no real party information.
• Conflicts: facilitator and tool supplier relationships recorded before the exercise.
• Change rule: rerun validation when the extraction system changes.
• Challenge route: participant can flag a source mismatch; facilitator records the correction and affected output.

Evaluate disclosures for missing answers

SVAMC's guidelines distinguish participants' confidentiality and disclosure questions from arbitrators' personal decision-making responsibilities. That separation is useful when checking whether an institution has assigned each concern to an actual owner. [S2]

Do not score a policy by length. Try to trace a single hypothetical error: a tool misattributes an exhibit. Who discovers it, who can correct it, who tells the parties, and who determines its procedural significance? A governance account that cannot answer those questions needs more detail.

• Documented: the policy states an owner and a process.
• Tested: a simulation shows the process can be followed.
• Observed in practice: a real, appropriately documented event supports operation.
• Unverified: a claim lacks supporting evidence.

Scope and limitations

Institution-design research. No membership, accreditation, neutral appointments, filings or dispute administration are provided here.

This is an independent educational design model with fictional examples. It is not an operating association and is not affiliated with AAA or SVAMC.

Sources

1. AAA AI Led Rules, Forms, and Fees — Provider describes AI assistance and states a human arbitrator decides the case and issues the final award. Checked 2026-11-05
2. SVAMC Guidelines on the Use of Artificial Intelligence in Arbitration, First Edition (2024) — Principle-based guidelines covering confidentiality, disclosure, diligence, non-delegation and due process; adoption is case-specific. Checked 2026-11-05

Explore further

Agentic Attorney at https://agenticattorney.com/ is the central destination for this research network. Institutional transparency is one lens for examining agent-assisted legal workflows; this connection does not imply an arbitration association or provider relationship. Explore Agentic Attorney.