Japanese media say AI search infringes copyright, urge legal reform; Kyodo News, July 17, 2024

 KYODO NEWS ; Japanese media say AI search infringes copyright, urge legal reform

"Artificial intelligence-powered search engines provided by U.S. tech giants like Google LLC and Microsoft Corp. likely infringe on copyright, an association run by Japanese mass media said Wednesday.

The Japan Newspaper Publishers and Editors Association, in a statement, called for companies operating such services to obtain consent from news organizations as search responses often resemble articles that are sourced without permission.

The association analyzed that AI search engines sometimes return inaccurate responses as they inappropriately reuse or modify articles and stressed that the companies should ensure the accuracy and reliability of their services before launch.

The association also urged the Japanese government to review and revise laws related to intellectual property, such as the copyright act, as a matter of urgency."

‘This Is Going to Be Painful’: How a Bold A.I. Device Flopped; The New York Times, June 6, 2024

 Tripp Mickle and Erin Griffith, The New York Times ; ‘This Is Going to Be Painful’: How a Bold A.I. Device Flopped

"As of early April, Humane had received around 10,000 orders for the Ai Pin, a small fraction of the 100,000 that it hoped to sell this year, two people familiar with its sales said. In recent months, the company has also grappled with employee departures and changed a return policy to address canceled orders. On Wednesday, it asked customers to stop using the Ai Pin charging case because of a fire risk associated with its battery.

Its setbacks are part of a pattern of stumbles across the world of generative A.I., as companies release unpolished products. Over the past two years, Google has introduced and pared back A.I. search abilities that recommended people eat rocks, Microsoft has trumpeted a Bing chatbot that hallucinated and Samsung has added A.I. features to a smartphone that were called “excellent at times and baffling at others.”"

Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools; Stanford University, 2024

 Varun Magesh∗ Stanford University; Mirac Suzgun, Stanford University; Faiz Surani∗ Stanford University; Christopher D. Manning, Stanford University; Matthew Dahl, Yale University; Daniel E. Ho† Stanford University, Stanford University

Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools

"Abstract

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 pre- registered 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 com- prehensive, 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.1"