AI in dispute resolution cannot be evaluated the same way we evaluate AI used for everyday business tasks. A tool that drafts a marketing email or summarizes a meeting transcript raises one set of concerns. A tool used in arbitration, mediation, or case assessment raises another.
The difference is trust. ADR depends on parties trusting that the process is fair, that confidential information remains protected, and that human judgment, not technology, ultimately governs decisions. As AI becomes more common in dispute resolution, ethics is no longer an academic discussion. It is the framework that determines whether technology strengthens or undermines confidence in the process.
These ethical considerations will be among the many topics explored at the 2026 AAA-ICDR Technology Conference, bringing together perspectives on AI, emerging technologies, and their evolving role in dispute resolution.
Protecting Trust Starts with Confidentiality and Privacy
Privacy is one of the foundations of ADR. Parties are willing to share sensitive information because they trust that it will remain protected, not publicly available, throughout the dispute resolution process.
That trust becomes even more important when AI tools are introduced. Disputes often involve contracts, pricing, trade secrets, personnel records, health information, financial data, settlement communications, and privileged material. Before any AI tool is used, participants should understand what information will be shared, who can access it, whether it will be retained, and whether it could be used to train a model.
These questions should be answered before a tool is used, not after.
For arbitrators, counsel, and parties, the safest assumption is that not every AI tool is appropriate for confidential dispute materials. Public or consumer-facing tools may not offer the protections a particular matter requires. Responsible AI use begins with selecting the right tool for the right purpose.
Bias must be addressed directly
Neutrality is a foundational value in ADR. AI does not change that. If anything, it makes neutrality more important because technology can create a false sense of objectivity.
AI systems may reflect bias through training data, design choices, prompts, user behavior, or the structure of a workflow. A system might overvalue certain forms of documentation, misunderstand less formal submissions, or fail to account for the way different parties present information.
Bias cannot be answered by assurance alone. It requires routine testing, monitoring, governance, and human review.
For legal teams evaluating AI tools, useful questions include:
- Has the tool been tested for the use case?
- How are outputs monitored?
- What happens when errors are found?
- Who is accountable for reviewing the output?
- Are both sides given a fair opportunity to present and clarify information?
- Does the process treat similarly situated parties consistently?
The right answer will depend on the tool and the proceeding, but the questions should not be skipped.
Transparency builds confidence
Transparency does not always require disclosing every technical detail. But parties should understand when AI is being used in a way that matters to the process.
In arbitration and mediation, surprises can damage trust. If AI is helping summarize submissions, organize exhibits, generate analysis, or support case assessment, participants should have a clear understanding of what the tool is and is not doing.
Transparency also means being honest about limits. AI may be useful for organizing information or identifying issues, but it – like a human – can make mistakes. It may miss context. It may produce a polished statement that still requires verification.
A transparent process helps participants understand where AI assists and where human judgment controls.
Accuracy requires verification
AI tools can be fast, useful, and wrong at the same time.
That is why legal professionals must verify outputs against the record. A summary should be checked against the submission. A citation should be checked against the source. An issue list should be compared to the pleadings, briefs, exhibits, and applicable rules.
This is especially important because AI errors can be easy to miss. A confident tone may make an unsupported statement appear reliable. A clean structure may conceal an omitted fact. A concise summary may flatten an important dispute.
In ADR, accuracy is not only a technical concern. It is a fairness concern.
Human oversight is essential
The most important safeguard in AI-enabled ADR is meaningful human oversight.
That does not mean a human casually approves whatever the system produces. It means a trained person with responsibility for the process reviews the output, tests it against the record, considers party feedback where appropriate, and exercises independent judgment.
For arbitrators, that means maintaining control over decision-making. For mediators, it means preserving the human work of understanding incentives, relationships, emotions, and practical barriers to settlement. For lawyers, it means using AI as a tool while remaining responsible for legal strategy, client advice, and the final pleadings or submissions.
Responsible AI is ongoing work
AI ethics is not a one-time checklist. Models change. Data changes. Use cases expand. Users learn new behaviors. A system that is appropriate for one workflow may not be appropriate for another.
Responsible ADR technology needs governance across the lifecycle: design, testing, deployment, monitoring, feedback, improvement, and retirement when needed.
The future of AI in ADR should be neither reflexive rejection nor blind adoption. The right path is disciplined use: protect confidentiality, test for bias, be transparent, verify outputs, preserve human judgment, and keep improving the process.
That is how AI can support ADR without weakening the principles that make ADR work. Join us at the 2026 AAA-ICDR Technology Conference to continue the conversation and explore how emerging technologies are shaping the future of dispute resolution.