Why We Pretend Massive Document Releases Can’t Be Analyzed Efficiently
When the Department of Justice releases terabytes of court filings, exhibits, and depositions—whether in the Epstein matter or other high-profile cases—a familiar narrative emerges: “No one can possibly read all this.” Commentators describe the disclosures as “a document dump,” implying deliberate obfuscation through volume. Pundits claim the material is functionally inaccessible without years of manual review. The message is clear: the truth is buried not by redaction, but by sheer scale.
This framing is increasingly disingenuous.
The technology to accelerate analysis of massive public document sets has existed for over a decade. Technology-Assisted Review (TAR)—using machine learning to prioritize, cluster, and surface relevant passages—has been court-approved since Da Silva Moore v. Publicis Groupe (2012) and is now standard practice in e-discovery. Modern extensions of this approach, combining optical character recognition, semantic search, and large language models, can compress months of manual skimming into days of attorney-guided inquiry—all while maintaining defensible accuracy rates above 85%.
This isn’t speculative. Law firms routinely deploy these tools to analyze millions of documents in complex litigation. Congressional oversight committees have begun experimenting with AI-augmented review for investigative work. The capability is real, accessible, and increasingly affordable.
Yet a strange taboo persists: we treat the existence of these tools as something to downplay rather than acknowledge. Why?
Partly because admitting that efficient analysis is feasible disrupts convenient narratives. If a document set can be meaningfully interrogated in weeks rather than years, then claims of “unsearchable volume” lose their rhetorical power. If journalists, researchers, or watchdog groups could—hypothetically—query a corpus for patterns, connections, or contradictions with precision, then the defense of “we just haven’t had time to review it all” becomes harder to sustain.
This isn’t an argument for careless automation. Responsible implementation requires attorney oversight, validation protocols, and ethical guardrails—especially when materials involve victims or sensitive personal information. The point isn’t that machines replace judgment; it’s that they eliminate artificial bottlenecks on human judgment. They shift the bottleneck from “reading everything” to “interpreting what matters”—a far more appropriate use of expert time.
More importantly, acknowledging feasibility doesn’t obligate anyone to act. Recognizing that a corpus could be analyzed efficiently doesn’t mean it should be analyzed without regard for privacy, trauma, or legal constraints. These are separate questions: capability versus responsibility. We can—and should—hold both truths simultaneously: the tools exist, and their application demands careful ethical calibration.
Pretending otherwise serves no constructive purpose. It infantilizes the public by implying that only those with unlimited resources can access truth. It lets institutions off the hook by accepting “volume” as an excuse for opacity. And it stalls necessary conversations about how to balance transparency, victim protection, and analytical efficiency in the age of AI.
The responsible path forward isn’t to deny capability exists. It’s to openly discuss:
- When AI-augmented review is appropriate for public materials
- How to design systems that protect vulnerable parties while enabling legitimate inquiry
- What standards should govern who gets access to analysis tools and under what conditions
We’ve moved beyond the era where “too many documents” is a credible barrier to accountability. The real question isn’t whether we can analyze massive releases efficiently—it’s whether we have the will to do so responsibly. Pretending the first question remains unanswered only delays progress on the second.
The tools are here. The expertise exists. The only thing buried under the mountain of paper is our willingness to acknowledge what’s possible—and then build the ethical frameworks to wield it wisely.