AI in Law & Legal Services · AI in E-Discovery & Document Review

What are the risks of relying on AI for document review in discovery?

Key risks of AI-assisted document review include missed relevant documents, inadequate validation, disputes over methodology, and over-reliance on the technology without sufficient human oversight.

Legal disclaimer

This page provides general information only and is not legal advice. Laws vary by jurisdiction and change over time. Consult a licensed attorney in your jurisdiction before making decisions based on this content.

Key takeaways

No AI-assisted review process is perfectly accurate, so there is always some risk that relevant or privileged documents are missed.
Inadequate statistical validation of the model's performance can undermine confidence in the review and invite challenges from opposing parties.
Disputes over the search terms, training documents, or sampling methodology used can create discovery disputes and additional litigation costs.
Over-reliance on AI output without sufficient human quality control can compound errors across a large document set.

No review process is perfectly accurate

Even well-implemented AI-assisted document review carries an inherent risk that some relevant, or even privileged, documents will be missed or misclassified. This isn’t unique to AI — manual review by humans is also imperfect — but the scale at which AI processes documents means a systematic error in how the model was trained can affect a much larger share of a collection than a single human reviewer’s mistake would. Understanding and managing this baseline risk is part of why validation and quality control remain central to any responsible AI-assisted review process.

Validation and methodology disputes

A significant risk specific to AI-assisted review involves how the process is validated and documented. If the statistical sampling used to test the model’s accuracy is inadequate, or if the training process itself is poorly documented, it becomes harder to defend the review if challenged by opposing counsel or scrutinized by a court. Disputes over search terms, the selection of training or “seed” documents, and sampling methodology are a recurring source of discovery disputes in cases using these methods, and resolving them can add cost and delay even when the underlying method is broadly accepted.

The danger of over-reliance without oversight

Perhaps the most significant practical risk is treating AI-assisted review as a “set it and forget it” solution rather than a process requiring ongoing human oversight. Attorneys remain responsible for ensuring that discovery obligations are reasonably satisfied regardless of what tool assists with the review, and courts have not treated AI assistance as a basis for relaxing that responsibility. Firms that skip adequate validation, fail to review edge cases the model flags as uncertain, or don’t document their process risk both producing an incomplete document set and having difficulty defending their approach if challenged later in the case.

Bottom line

The main risks of relying on AI for discovery document review include missed or misclassified documents, disputes over validation and methodology, and the danger of insufficient human oversight — all of which make careful process design and documentation essential rather than optional.

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Important caveats

The severity of these risks depends heavily on how carefully the review process is designed and validated in a given case.
This is general information, not legal advice about managing discovery risk in any specific matter.

Frequently asked questions

Can AI-assisted review miss privileged documents?

Yes — like any review process, it's possible for the model to misclassify privileged documents, which is why many workflows include additional privilege-specific review steps as a safeguard.

What happens if opposing counsel challenges the AI review methodology?

This can lead to discovery disputes requiring the producing party to explain and defend the training process, sampling, and validation statistics used, sometimes requiring court intervention to resolve.

Does using AI reduce an attorney's discovery obligations?

No — attorneys remain responsible for ensuring discovery obligations are reasonably met regardless of what technology is used in the review process.

Sources

[1]E-discovery standards and practice resources — American Bar Association
[2]Federal court rules and discovery guidance — United States Courts

Written by Editorial Team

Last updated July 28, 2026

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