Connecticut Supreme Court Asked to Dismiss Case Due to AI
Connecticut Supreme Court Asked to Dismiss Case Due to AI
Publish Date: 2026-03-03 14:57:00
Source Domain: www.govtech.com
(TNS) — Questions about fake legal citations created by artificial intelligence and overlooked due to lawyers’ lax proofreading are before the Connecticut Supreme Court.
A central case involves a landlord’s attempt to evict a Middletown tenant who objected to a rent hike and was backed by the town’s Fair Rent Commission. Lawyers for the Brooklyn, N.Y.-based landlord submitted a brief to a lower court containing “hallucinatory” citations created by generative AI, according to a brief to the Supreme Court that included work by students with the Yale Law School-based Jerome N. Frank Legal Services Organization.
For example, a quoted phrase in a citation by the plaintiff’s lawyers, Wallingford-based GLG Law LLC, does not appear in the cited case “nor has any other court ever written this phrase,” the brief says.
The promise and pitfalls of AI have been much-discussed in legal circles across the nation.
In December, the American Bar Association’s Task Force on Law and Artificial Intelligence released a report that includes guidance and recommendations meant to “uphold our profession’s core values of competence, integrity, and public trust,” the organization’s immediate past president, William R. Bay, wrote in the introduction.
“The future of our profession will be shaped by how we meet this moment,” Bay wrote. “Together, we will work to promote a future where AI serves both our clients and the public good.”
The use of falsely generated citations, according to the brief by the Yale-based legal services organization, “is dangerous as it suggests nonexistent precedent for the plaintiff’s arguments.”
“Such citations — in these cases and beyond — are unfair to opposing parties,” the brief says. “Falsely generated case citations may be difficult to identify and detect, particularly when they are rampant throughout a 60-page brief. They especially harm self-represented or disadvantaged parties, who may…