{"id":141850,"date":"2026-01-14T17:06:29","date_gmt":"2026-01-15T01:06:29","guid":{"rendered":"https:\/\/xira.com\/p\/2026\/01\/14\/warning-party-to-stop-citing-fake-ai-cases-is-not-in-fact-bias\/"},"modified":"2026-01-14T17:06:29","modified_gmt":"2026-01-15T01:06:29","slug":"warning-party-to-stop-citing-fake-ai-cases-is-not-in-fact-bias","status":"publish","type":"post","link":"https:\/\/xira.com\/p\/2026\/01\/14\/warning-party-to-stop-citing-fake-ai-cases-is-not-in-fact-bias\/","title":{"rendered":"Warning Party To Stop Citing Fake AI Cases Is Not, In Fact, Bias"},"content":{"rendered":"

Phony cases continue to proliferate across the docket. This recent explosion stems from the advent of artificial intelligence tools, with over 700 instances of embarrassing hallucinations working their way into filings so far<\/a>. The problem will inevitably get worse since these AI tools are eager to provide users with whatever answer they desire, even if it\u2019s wholly made-up garbage. That\u2019s not entirely the fault of the AI. A non-savvy user is more likely to prompt the tool in ways that incentivize the algorithms to produce results to match the user\u2019s request. Ask a large language model for key landlord-tenant citations and it will \u2014 often \u2014 do a decent job. Ask it to provide case citations for the proposition that my bonkers argument is actually correct, and it has a much higher chance of going off the rails.<\/p>\n

Some tools have more robust safeguards than others, but, at the end of the day, a large language model wants<\/em> to give the user what it wants. That\u2019s trouble if the user makes the wrong ask and isn\u2019t careful about checking the work of their semi-random word generator.<\/p>\n

While lawyers keep screwing this up, the pro se<\/em> litigant presents a vector for hallucinatory infection. They\u2019re already up against it with a system they don\u2019t fully understand and AI provides easy, seemingly right answers. If AI is mansplaining-as-a-service<\/a> \u2014 exceedingly confident, regardless of accuracy \u2014 then its most trusting victims will be people just trying to figure out how to enforce their rights.<\/p>\n

And it\u2019s a problem bound to get worse because AI is cheap and lawyers are expensive.<\/p>\n

That said, once the courts warn a litigant to stop using AI, that should be the end of it. One litigant, however, went the other direction and claimed the court\u2019s warning proved its bias against his case. <\/p>\n

This argument fared\u2026 poorly. <\/p>\n

\n

Finally, Plaintiffs [sic] objects to the Magistrate Judge\u2019s \u201ccriticism\u201d of his use of artificial intelligence to cite to non-existent case law and errors in other citations. Id. at 3 (citing Non-Final R&R at 2-4). Notwithstanding that a review of Plaintiffs \u201ccitations\u201d proves the Magistrate Judge\u2019s point, the warning given by the Magistrate Judge with respect to Plaintiffs future filings had no impact on the full analysis conducted by the Magistrate Judge on each of Plaintiff s claims.<\/p>\n<\/blockquote>\n

Rob Freund (who flagged the opinion on the platform formerly known as Twitter) offered the friendly advice \u201cIf a judge calls you out for citing \u201cnon-existent case law,\u201d filing an objection in response is probably not the play<\/a>.\u201d<\/p>\n

The plaintiff\u2019s specific objection was that the magistrate judge\u2019s warning was unclear:<\/p>\n

No Specific Misquotes; Opposing Misstatements, Overlooked [Doc. 65 p. 2] R&R Error: Vague \u201cAI-generated, incorrect laws\u201d claim [citing Doc. 12-1] without examples.<\/p>\n

The problem with this claim is that there were, in fact, multiple specific examples <\/em>of false citations. They were laid out by opposing counsel in its motion. Most notably the response to plaintiff\u2019s citing \u201cSolomon v. Norwest Corp.<\/em>, 546 S.E.2d 330 (Ga. 2001),\u201d prompting opposing counsel to write:<\/p>\n

\n

The citation of \u201c546 S.E.2d 330\u201d is actually for the case Nunley v. Nunley, 248 Ga. App. 208, 546 S.E.2d 330 (2001), involving a hen farm partnership.<\/p>\n<\/blockquote>\n

Talk about laying an egg. <\/p>\n

(Full opinion on the next page\u2026)<\/em><\/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. Joe also serves as a Managing Director at RPN Executive Search<\/a>.<\/em><\/strong><\/p>\n<\/p>\n

The post Warning Party To Stop Citing Fake AI Cases Is Not, In Fact, Bias<\/a> appeared first on Above the Law<\/a>.<\/p>\n

Phony cases continue to proliferate across the docket. This recent explosion stems from the advent of artificial intelligence tools, with over 700 instances of embarrassing hallucinations working their way into filings so far<\/a>. The problem will inevitably get worse since these AI tools are eager to provide users with whatever answer they desire, even if it\u2019s wholly made-up garbage. That\u2019s not entirely the fault of the AI. A non-savvy user is more likely to prompt the tool in ways that incentivize the algorithms to produce results to match the user\u2019s request. Ask a large language model for key landlord-tenant citations and it will \u2014 often \u2014 do a decent job. Ask it to provide case citations for the proposition that my bonkers argument is actually correct, and it has a much higher chance of going off the rails.<\/p>\n

Some tools have more robust safeguards than others, but, at the end of the day, a large language model wants<\/em> to give the user what it wants. That\u2019s trouble if the user makes the wrong ask and isn\u2019t careful about checking the work of their semi-random word generator.<\/p>\n

While lawyers keep screwing this up, the pro se<\/em> litigant presents a vector for hallucinatory infection. They\u2019re already up against it with a system they don\u2019t fully understand and AI provides easy, seemingly right answers. If AI is mansplaining-as-a-service<\/a> \u2014 exceedingly confident, regardless of accuracy \u2014 then its most trusting victims will be people just trying to figure out how to enforce their rights.<\/p>\n

And it\u2019s a problem bound to get worse because AI is cheap and lawyers are expensive.<\/p>\n

That said, once the courts warn a litigant to stop using AI, that should be the end of it. One litigant, however, went the other direction and claimed the court\u2019s warning proved its bias against his case. <\/p>\n

This argument fared\u2026 poorly. <\/p>\n

\n

Finally, Plaintiffs [sic] objects to the Magistrate Judge\u2019s \u201ccriticism\u201d of his use of artificial intelligence to cite to non-existent case law and errors in other citations. Id. at 3 (citing Non-Final R&R at 2-4). Notwithstanding that a review of Plaintiffs \u201ccitations\u201d proves the Magistrate Judge\u2019s point, the warning given by the Magistrate Judge with respect to Plaintiffs future filings had no impact on the full analysis conducted by the Magistrate Judge on each of Plaintiff s claims.<\/p>\n<\/blockquote>\n

Rob Freund (who flagged the opinion on the platform formerly known as Twitter) offered the friendly advice \u201cIf a judge calls you out for citing \u201cnon-existent case law,\u201d filing an objection in response is probably not the play<\/a>.\u201d<\/p>\n

The plaintiff\u2019s specific objection was that the magistrate judge\u2019s warning was unclear:<\/p>\n

No Specific Misquotes; Opposing Misstatements, Overlooked [Doc. 65 p. 2] R&R Error: Vague \u201cAI-generated, incorrect laws\u201d claim [citing Doc. 12-1] without examples.<\/p>\n

The problem with this claim is that there were, in fact, multiple specific examples <\/em>of false citations. They were laid out by opposing counsel in its motion. Most notably the response to plaintiff\u2019s citing \u201cSolomon v. Norwest Corp.<\/em>, 546 S.E.2d 330 (Ga. 2001),\u201d prompting opposing counsel to write:<\/p>\n

\n

The citation of \u201c546 S.E.2d 330\u201d is actually for the case Nunley v. Nunley, 248 Ga. App. 208, 546 S.E.2d 330 (2001), involving a hen farm partnership.<\/p>\n<\/blockquote>\n

Talk about laying an egg. <\/p>\n

(Full opinion on the next page\u2026)<\/em><\/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. Joe also serves as a Managing Director at RPN Executive Search<\/a>.<\/em><\/strong><\/p>\n<\/p>\n

The post Warning Party To Stop Citing Fake AI Cases Is Not, In Fact, Bias<\/a> appeared first on Above the Law<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Phony cases continue to proliferate across the docket. This recent explosion stems from the advent of artificial intelligence tools, with over 700 instances of embarrassing hallucinations working their way into filings so far. The problem will inevitably get worse since these AI tools are eager to provide users with whatever answer they desire, even if […]<\/p>\n","protected":false},"author":3,"featured_media":141836,"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-141850","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\/01\/Headshot-300x200-mDkg40.jpg?fit=300%2C200&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/141850","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=141850"}],"version-history":[{"count":0,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/141850\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media\/141836"}],"wp:attachment":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media?parent=141850"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/categories?post=141850"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/tags?post=141850"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}