How The JAMS AI Rules Can Streamline Discovery For AI-Related Disputes

The emergence of AI has introduced significant challenges to the e-discovery process. Even without factoring in AI, the identification, collection, and analysis of electronically stored information (ESI) 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 when navigating e-discovery. One of the foremost issues is the volume and variety of data generated by AI systems. Unlike traditional software, which stores data in structured formats, AI applications often generate unstructured data, such as chat logs, sensor data, and machine learning model outputs, that can be difficult to categorize or analyze. Moreover, AI models themselves may evolve over time, learning from new data and altering their outputs, making it harder to reproduce or trace decision-making processes for legal review.

Interpreting Complex AI Systems and Understanding the Legal Implications

Another key challenge lies in the interpretability of AI systems. Many AI models, particularly those based on deep learning, function as "black boxes," making it difficult to determine how decisions were made. When a legal matter hinges on understanding why an AI system behaved in a particular way, such as in discrimination or liability cases, e-discovery may require access to not just the data, but also the model architecture, training datasets, and configuration settings. This level of complexity is often outside the expertise of traditional legal teams and can require technical experts to decode.

Additionally, data ownership and privacy concerns complicate the e-discovery process. AI systems often process data from multiple sources, some of which may contain proprietary or personal information subject to privacy laws such as the EU's General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Legal teams must ensure that the extraction and review of this data comply with all applicable regulations, adding layers of legal and ethical scrutiny.

Often, there are also significant concerns regarding preservation and spoliation. AI systems that continuously learn and update pose a risk to evidence preservation, as the underlying data and model parameters will 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 time of a disputed event.

The JAMS AI Dispute Resolution Rules: A Framework for Modern E-Discovery

Recognizing these evolving challenges, particularly the difficulty of preserving, interpreting and managing AI-generated evidence, JAMS developed its AI Disputes Clause and Rules. As one of the principal contributors to the drafting of these rules, I helped shape a framework designed to provide both a structure and flexibility for resolving disputes involving AI technologies. One of the most significant benefits of these rules is their ability to streamline e-discovery, a historically burdensome and costly phase in legal proceedings.

Traditional legal frameworks are ill-equipped to efficiently manage the vast amounts of unstructured and dynamic data that are often at issue in AI-related disputes. The JAMS AI 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 these rules are empowered to limit overly broad or irrelevant discovery requests, reducing time and expense while focusing on truly material evidence. The procedural flexibility allowed by the JAMS AI Rules allows for the arbitrator and parties to work together to meet the rapidly evolving challenges of discovery from AI systems.

A key innovation in the JAMS framework is the 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 technical nuances of AI, which helps expedite the review of complex digital evidence, where appropriate. This minimizes the risk of misunderstandings and allows for more targeted and effective discovery efforts.

Moreover, the JAMS AI Rules promote proportionality in discovery so that requests are balanced against the burden and relevance of the data sought—an essential safeguard when dealing with opaque or evolving AI systems. The rules also support confidentiality measures to protect sensitive proprietary information, which is often a concern in AI-related cases (see the JAMS Artificial Intelligence Dispute Rules Protective Order template).

Leveraging Technically Informed Arbitration in AI-Related Disputes

Another big advantage of arbitration for AI-related disputes is that it allows the parties to select an arbitrator with technical and e-discovery knowledge, which most judges don't have. This can be a huge cost and time saver in discovery, as the arbitrator can more efficiently resolve issues related to the scope of discovery, search terms, proportionality and the AI systems themselves. Appointing an arbitrator with knowledge about AI systems can also produce more fair and reasonable rulings on the merits in a developing legal landscape where key issues might not have much legal precedent.

A Practical Path Forward for E-Discovery in the AI Era

In short, by providing structure, flexibility and technical competence, the JAMS AI Rules offer a pragmatic solution to managing e-discovery challenges in AI disputes, which can save time, reduce costs and promote fair outcomes.

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