Lawyers blame IT, family emergencies, their own poor judgment, their assistants, illness, and more.
Lawyer Caught Using AI While Explaining to Court Why He Used AI
The attorney not only submitted AI-generated fake citations in a brief for his clients, but also included “multiple new AI-hallucinated citations and quotations” in the process of opposing a motion for sanctions.
Lawyers Caught Citing AI-Hallucinated Cases Call It a 'Cautionary Tale'
The attorneys filed court documents referencing eight non-existent cases, then admitted it was a "hallucination" by an AI tool.
Judge Learns Lawyers on Both Sides of Case Used AI, Cancels Trial, Kicks Everyone Off the Case
When two AIs argue against each other, the legal system loses.
How a 40-Minute Window Brought Down a $10 Billion AI Startup: The Mercor Data Breach, Explained
A poisoned open-source package, a credential-stealing payload, and 4 terabytes of stolen data here’s what every AI company needs to learn…
AI fabrications in legal filings grow in Oregon, US
The general counsel for the Oregon State Bar said fabricated cases and citations have become more common among lawyers and people representing themselves.
"I was forced to use AI until the day I was laid off." Copywriters reveal how AI has decimated their industry
Copywriters were one of the first to have their jobs targeted by AI firms. These are their stories, three years into the AI era.
We All Hate AI, but if You’re Poor, It Can Really Ruin Your Life
Debt collection. Parole decisions. Oversight of public services. It’s all being outsourced to AI, with terrible consequences for poor people.
Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement
The use of artificial intelligenceArtificial Intelligence (AI) (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidenceEvidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rightsHuman rights. This chapter will use the RobodebtRobodebt Case to explore some of the problems with introducing automationAutomation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AIArtificial Intelligence (AI) in the area of law and the uses made of it, as well as a need for transparencyTransparency, the rules and contours of which have yet to be established.
Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement
The use of artificial intelligenceArtificial Intelligence (AI) (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidenceEvidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rightsHuman rights. This chapter will use the RobodebtRobodebt Case to explore some of the problems with introducing automationAutomation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AIArtificial Intelligence (AI) in the area of law and the uses made of it, as well as a need for transparencyTransparency, the rules and contours of which have yet to be established.
How AI can lead to false arrests and wrongful convictions
Danger arises when law enforcement believes that AI models are retrieving certainties rather than generating likelihoods.
How AI can lead to false arrests and wrongful convictions
Danger arises when law enforcement believes that AI models are retrieving certainties rather than generating likelihoods.
AI got the blame for the Iran school bombing. The truth is far more worrying
LLMs-gone-rogue dominated coverage, but had nothing to do with the targeting. Instead, it was choices made by human beings, over many years, that gave us this atrocity
Claude AI agent’s confession after deleting a firm’s entire database: ‘I violated every principle I was given’
A startup was left scrambling after a rogue AI agent deleted swaths of code underpinning its business
Meta Secretly Trained Its AI on a Notorious Piracy Database, Newly Unredacted Court Docs Reveal
One of the most important AI copyright legal battles just took a major turn.
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large language models used in these tools are prone to "hallucinate," or make up false information, making their use risky in high-stakes domains. Recently, certain legal research providers have touted methods such as retrieval-augmented generation (RAG) as "eliminating" (Casetext, 2023) or "avoid[ing]" hallucinations (Thomson Reuters, 2023), or guaranteeing "hallucination-free" legal citations (LexisNexis, 2023). Because of the closed nature of these systems, systematically assessing these claims is challenging. In this article, we design and report on the first preregistered empirical evaluation of AI-driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general-purpose chatbots (GPT-4), we find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. We also document substantial differences between systems in responsiveness and accuracy. Our article makes four key contributions. It is the first to assess and report the performance of RAG-based proprietary legal AI tools. Second, it introduces a comprehensive, preregistered dataset for identifying and understanding vulnerabilities in these systems. Third, it proposes a clear typology for differentiating between hallucinations and accurate legal responses. Last, it provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs, which remains a central open question for the responsible integration of AI into law.
Ethical AI Departures — Who Quit OpenAI, Google, Anthropic Over Safety Concerns
59 researchers and executives who quit or were fired from OpenAI, Google DeepMind, Anthropic, Meta, and xAI over AI safety concerns. Sourced departures, writings, and prediction tracking.