{"id":157710,"date":"2026-07-21T16:08:37","date_gmt":"2026-07-22T00:08:37","guid":{"rendered":"https:\/\/xira.com\/p\/2026\/07\/21\/lawyers-learning-you-cant-square-peg-ai-into-ediscovery-round-holes\/"},"modified":"2026-07-21T16:08:37","modified_gmt":"2026-07-22T00:08:37","slug":"lawyers-learning-you-cant-square-peg-ai-into-ediscovery-round-holes","status":"publish","type":"post","link":"https:\/\/xira.com\/p\/2026\/07\/21\/lawyers-learning-you-cant-square-peg-ai-into-ediscovery-round-holes\/","title":{"rendered":"Lawyers Learning You Can\u2019t Square Peg AI Into eDiscovery Round Holes"},"content":{"rendered":"

Attorneys can do a lot with new generative AI tools beyond earn sanctions for faking citations. But despite the AI industry\u2019s curiously apocalyptic advertising claims \u2014 like Anthropic\u2019s World Cup ad that sort of implied AI would kill us all?<\/a> \u2014 the fact of the matter is that large language models can\u2019t do everything right now. Indeed, for a lot of tasks, they\u2019re affirmatively worse than the technology and processes that we\u2019ve refined over the past decade or so.<\/p>\n

A new report<\/a> from Casepoint<\/a> titled \u201cFrom AI Hype to AI Accountability: What You Can Learn From Legal and FOIA Teams About AI Modernization,\u201d built on practitioner interviews with legal, records, and FOIA teams, covers a broad array of AI topics. One that jumped out is the admonition that lawyers need to understand that this new technology may excite the legal community, but when it comes to eDiscovery, the old ways are still the best.<\/p>\n

\u201cPrivilege review is one example,\u201d the report explains. \u201cThe commercial appeal is obvious: privilege review is expensive, time-consuming, and often pattern-driven.\u201d The problem is that mistakes can be devastating and users all too often trust AI\u2019s confident, mansplaining-as-a-service<\/a> output without performing needed diligence. \u201cAl may still have a role in privilege review, responsiveness calls, redactions, FOIA exemptions, legal advice, and production decisions,\u201d the report continues, but teams \u201cshould know how output will be checked, what human remains accountable, what documentation will be retained, and whether the process can be explained later.\u201d<\/p>\n

Conveniently, we already have these processes for existing technologies:<\/p>\n

\n

GenAl also lacks the mature validation history that helped technology-assisted review gain acceptance. TAR became defensible because practitioners developed ways to explain and test the process, including recall, precision, seed sets, sampling, and quality-control protocols. GenAl review has not yet reached the same level of accepted process maturity.<\/p>\n<\/blockquote>\n

As a user at a large federal civilian agency explained, \u201cThere\u2019s no point using AI in the manner I described if we get to a production, go to court, and they say: this is not verifiable, this is not TAR 2.0, and I don\u2019t have any of the statistics and measures I need.\u201d<\/p>\n

We\u2019ve built technology to perform these tasks that doesn\u2019t have the hankering to take black box flights of hallucinatory fancy and spent years making sure it had the reliability and accountability to hold up in court. The Casepoint interviewees describe hallucinations not as a bug to be patched but as a permanent \u201cworkflow reality.\u201d TAR had plenty of flaws. Confidently inventing a case that does not exist was not among them. Vendors are building generative AI into their systems to handle what it\u2019s suited to handle, and metric tons of digital ink have been spilled describing the efforts legal tech providers have made to build out genuine audit processes, but part of adopting AI is understanding its limits. <\/p>\n

It\u2019s been 14 years since Magistrate Judge Andrew Peck issued the first judicial opinion<\/a> approving predictive coding, in Da Silva Moore v. Publicis Groupe<\/em>. The crux of the case wasn\u2019t \u201ccomputers are good,\u201d but that defensibility lives in the process<\/em>. TAR didn\u2019t become boringly trustworthy because the technology was magic. We just poured years into forging a system we could test, measure, and explain to a judge. Part of selling the courts on the rules was using (for the most part) rules-based technology.<\/p>\n

But the selling point of the \u201cagentic\u201d era is that it comes up with its own rules to match the goal. That doesn\u2019t mean it\u2019s always wrong \u2014 the rules it comes up with may well be defensible \u2014 but it\u2019s a different task to explain that to a judge.<\/p>\n

Though so far, the courts don\u2019t seem to think so. In Schulte v. LinkedIn<\/em>, decided in the Northern District of California this June, Judge Eumi Lee declined to invent a special legal framework for generative AI in discovery and simply analyzed it under the principles courts already apply to TAR. At a high enough level this is true \u2014 it has to consistently display recall, precision, and a testable process. But it also hamstrings the technology a bit. It\u2019s not quite like demanding that cars be built as mechanical horses, but some of the old processes just won\u2019t get the most out of the new technologies.<\/p>\n

\n

The practical advice is simple: do not treat Al governance as policy language alone. Build it into the workflow. Legal teams need to define what Al may do, what information it may access, what records it creates, how output is reviewed, and how decisions are documented. Without that structure, organizations may not discover the accountability gap until a matter, FOIA request, production, court challenge, or oversight review forces the issue.<\/p>\n<\/blockquote>\n

None of this means generative AI won\u2019t take on more and more discovery tasks or that courts won\u2019t eventually develop rules optimized to the technology\u2026 but it also means everyone needs to be a little honest about what it\u2019s not capable of doing. <\/p>\n

\n

\"Headshot\"Joe Patrice<\/a>\u00a0is a senior editor at Above the Law and co-host of Thinking Like A Lawyer<\/a>. Feel free to\u00a0email<\/a> any tips, questions, or comments. Follow him on\u00a0Twitter<\/a>\u00a0or Bluesky<\/a> if you\u2019re interested in law, politics, and a healthy dose of college sports news.<\/em><\/strong><\/p>\n

The post Lawyers Learning You Can\u2019t Square Peg AI Into eDiscovery Round Holes<\/a> appeared first on Above the Law<\/a>.<\/p>\n

Attorneys can do a lot with new generative AI tools beyond earn sanctions for faking citations. But despite the AI industry\u2019s curiously apocalyptic advertising claims \u2014 like Anthropic\u2019s World Cup ad that sort of implied AI would kill us all?<\/a> \u2014 the fact of the matter is that large language models can\u2019t do everything right now. Indeed, for a lot of tasks, they\u2019re affirmatively worse than the technology and processes that we\u2019ve refined over the past decade or so.<\/p>\n

A new report<\/a> from Casepoint<\/a> titled \u201cFrom AI Hype to AI Accountability: What You Can Learn From Legal and FOIA Teams About AI Modernization,\u201d built on practitioner interviews with legal, records, and FOIA teams, covers a broad array of AI topics. One that jumped out is the admonition that lawyers need to understand that this new technology may excite the legal community, but when it comes to eDiscovery, the old ways are still the best.<\/p>\n

\u201cPrivilege review is one example,\u201d the report explains. \u201cThe commercial appeal is obvious: privilege review is expensive, time-consuming, and often pattern-driven.\u201d The problem is that mistakes can be devastating and users all too often trust AI\u2019s confident, mansplaining-as-a-service<\/a> output without performing needed diligence. \u201cAl may still have a role in privilege review, responsiveness calls, redactions, FOIA exemptions, legal advice, and production decisions,\u201d the report continues, but teams \u201cshould know how output will be checked, what human remains accountable, what documentation will be retained, and whether the process can be explained later.\u201d<\/p>\n

Conveniently, we already have these processes for existing technologies:<\/p>\n

\n

GenAl also lacks the mature validation history that helped technology-assisted review gain acceptance. TAR became defensible because practitioners developed ways to explain and test the process, including recall, precision, seed sets, sampling, and quality-control protocols. GenAl review has not yet reached the same level of accepted process maturity.<\/p>\n<\/blockquote>\n

As a user at a large federal civilian agency explained, \u201cThere\u2019s no point using AI in the manner I described if we get to a production, go to court, and they say: this is not verifiable, this is not TAR 2.0, and I don\u2019t have any of the statistics and measures I need.\u201d<\/p>\n

We\u2019ve built technology to perform these tasks that doesn\u2019t have the hankering to take black box flights of hallucinatory fancy and spent years making sure it had the reliability and accountability to hold up in court. The Casepoint interviewees describe hallucinations not as a bug to be patched but as a permanent \u201cworkflow reality.\u201d TAR had plenty of flaws. Confidently inventing a case that does not exist was not among them. Vendors are building generative AI into their systems to handle what it\u2019s suited to handle, and metric tons of digital ink have been spilled describing the efforts legal tech providers have made to build out genuine audit processes, but part of adopting AI is understanding its limits. <\/p>\n

It\u2019s been 14 years since Magistrate Judge Andrew Peck issued the first judicial opinion<\/a> approving predictive coding, in Da Silva Moore v. Publicis Groupe<\/em>. The crux of the case wasn\u2019t \u201ccomputers are good,\u201d but that defensibility lives in the process<\/em>. TAR didn\u2019t become boringly trustworthy because the technology was magic. We just poured years into forging a system we could test, measure, and explain to a judge. Part of selling the courts on the rules was using (for the most part) rules-based technology.<\/p>\n

But the selling point of the \u201cagentic\u201d era is that it comes up with its own rules to match the goal. That doesn\u2019t mean it\u2019s always wrong \u2014 the rules it comes up with may well be defensible \u2014 but it\u2019s a different task to explain that to a judge.<\/p>\n

Though so far, the courts don\u2019t seem to think so. In Schulte v. LinkedIn<\/em>, decided in the Northern District of California this June, Judge Eumi Lee declined to invent a special legal framework for generative AI in discovery and simply analyzed it under the principles courts already apply to TAR. At a high enough level this is true \u2014 it has to consistently display recall, precision, and a testable process. But it also hamstrings the technology a bit. It\u2019s not quite like demanding that cars be built as mechanical horses, but some of the old processes just won\u2019t get the most out of the new technologies.<\/p>\n

\n

The practical advice is simple: do not treat Al governance as policy language alone. Build it into the workflow. Legal teams need to define what Al may do, what information it may access, what records it creates, how output is reviewed, and how decisions are documented. Without that structure, organizations may not discover the accountability gap until a matter, FOIA request, production, court challenge, or oversight review forces the issue.<\/p>\n<\/blockquote>\n

None of this means generative AI won\u2019t take on more and more discovery tasks or that courts won\u2019t eventually develop rules optimized to the technology\u2026 but it also means everyone needs to be a little honest about what it\u2019s not capable of doing. <\/p>\n

\n

\"Headshot\"Joe Patrice<\/a>\u00a0is a senior editor at Above the Law and co-host of Thinking Like A Lawyer<\/a>. Feel free to\u00a0email<\/a> any tips, questions, or comments. Follow him on\u00a0Twitter<\/a>\u00a0or Bluesky<\/a> if you\u2019re interested in law, politics, and a healthy dose of college sports news.<\/em><\/strong><\/p>\n

The post Lawyers Learning You Can\u2019t Square Peg AI Into eDiscovery Round Holes<\/a> appeared first on Above the Law<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Attorneys can do a lot with new generative AI tools beyond earn sanctions for faking citations. But despite the AI industry\u2019s curiously apocalyptic advertising claims \u2014 like Anthropic\u2019s World Cup ad that sort of implied AI would kill us all? \u2014 the fact of the matter is that large language models can\u2019t do everything right […]<\/p>\n","protected":false},"author":3,"featured_media":157694,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16],"tags":[],"class_list":["post-157710","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-above_the_law"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/xira.com\/p\/wp-content\/uploads\/2026\/07\/Headshot-300x200-tLZ1Tn.jpg?fit=300%2C200&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/157710","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/comments?post=157710"}],"version-history":[{"count":0,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/157710\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media\/157694"}],"wp:attachment":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media?parent=157710"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/categories?post=157710"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/tags?post=157710"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}