The emergence of AI technologies has introduced significant challenges to the process of e-discovery. Even without factoring in AI, the identification, collection, and analysis of electronically stored information has exploded in scope and complexity in recent years. As AI systems become increasingly embedded in both personal and corporate environments, legal professionals must confront a range of new challenges that the conventional discovery toolkit was never designed to handle.
§ 01 · Why AI breaks conventional discovery
The foremost issue is the volume and variety of data AI systems generate. Unlike traditional software that stores data in structured formats, AI applications often produce unstructured output — chat logs, sensor data, machine-learning model outputs — that is difficult to categorize or analyze. Worse, the models themselves may evolve over time, learning from new data and altering their outputs, which makes it harder to reproduce or trace a decision-making process for legal review.
A second challenge is interpretability. Many AI models, particularly those built on deep learning, function as black boxes. When a legal matter hinges on understanding why an AI system behaved a particular way — as in discrimination or liability cases — discovery may require access not just to the data but to the model architecture, the training datasets, and the configuration settings. That level of complexity sits outside the expertise of most legal teams and requires technical experts to decode.
Layered on top are data ownership and privacy concerns. AI systems often process data from multiple sources, some proprietary and some containing personal information subject to privacy laws such as the GDPR or the CCPA, and extraction and review must comply with all of them. And there are significant preservation and spoliation risks: a system that continuously learns and updates threatens evidence preservation, because the underlying data and model parameters change over time. Without a clear snapshot or audit trail, it can be nearly impossible to recover the state of an AI system at the moment of the disputed event.
§ 02 · What the JAMS AI Rules actually do
JAMS introduced its AI Disputes Rules to address precisely these challenges, and one of their most significant benefits is the ability to streamline e-discovery — historically the most burdensome and costly phase of a proceeding. Traditional legal frameworks are ill-equipped to efficiently manage the vast amounts of unstructured, dynamic data at issue in AI-related disputes. The rules address this by allowing the parties to work with a technically savvy arbitrator or discovery referee to tailor discovery protocols to the realities of the AI technology at issue. Arbitrators under the rules are empowered to limit overly broad or irrelevant requests, reducing time and expense while keeping the focus on truly material evidence.
A key innovation in the framework is its focus on early identification of technical issues, including data provenance and model explainability. The rules allow for the appointment of neutral experts who understand both the legal and the technical nuances of AI, which helps expedite the review of complex digital evidence where appropriate and minimizes the risk of misunderstandings. The rules also promote proportionality in discovery — balancing each request against the burden and relevance of the data sought, an essential safeguard when the system in question is opaque or evolving — and support confidentiality measures to protect sensitive proprietary information, a recurring concern in AI-related cases.
Traditional frameworks are ill-equipped for the unstructured, evolving data at the center of AI disputes. The rules let the parties tailor discovery to the technology itself.
§ 03 · The arbitrator-selection advantage
Arbitration offers AI litigants one further advantage: the parties select the decision-maker. An arbitrator with technical and e-discovery knowledge that most judges do not have can resolve disputes over the scope of discovery, search terms, proportionality, and the AI systems themselves far more efficiently — a substantial cost and time saver. Technical fluency also matters on the merits. In a developing legal landscape where key issues may have little precedent, an arbitrator who understands AI systems is better positioned to produce fair and reasonable rulings.
§ 04 · Putting the rules to work
For counsel drafting or managing AI-related agreements, the practical sequence is straightforward: (1) address dispute resolution before the dispute, by adopting the JAMS AI Rules in the contract's dispute-resolution clause; (2) raise the technical issues — data provenance, explainability, preservation of model state — at the earliest procedural conference, not after positions have hardened; (3) insist on an arbitrator or discovery referee with demonstrated AI and e-discovery fluency; and (4) build proportionality limits and confidentiality protections into the discovery protocol from the outset.
In short, by providing structure, flexibility, and technical competence, the JAMS AI Rules offer a pragmatic solution to managing the e-discovery challenges of AI disputes — one that saves time, reduces cost, and promotes fair outcomes. The technology at issue will keep evolving. The discovery framework should be built, from day one, to evolve with it.
Adapted from “How the JAMS AI Rules Can Streamline Discovery for AI-Related Disputes” (2025).