YouTube's recommendation algorithm prioritizes non-Kyrgyz content for Kyrgyz children, validating community concerns about cultural and linguistic erosion.
v8i1.14550,https://ojs.aaai.org/index.php/ICWSM/article/view/14550
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EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
AIPsy-Affect supplies 480 keyword-free clinical vignettes and matched neutral controls for mechanistic interpretability studies of emotion in language models.
SUMMIR is a multimetric ranking model that orders LLM-generated sports insights by importance while incorporating hallucination detection to improve factual reliability across cricket, soccer, basketball, and baseball articles.
DMICF models interactions from user- and item-centric perspectives with a macro-micro prototype-aware variational encoder and dimension-wise intent alignment to improve collaborative filtering.
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Empire Amplifier: Uncovering and Contesting the Prioritization of Colonial Content on Platforms Through Community-Informed Algorithmic Auditing
YouTube's recommendation algorithm prioritizes non-Kyrgyz content for Kyrgyz children, validating community concerns about cultural and linguistic erosion.
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AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
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AIPsy-Affect: A Keyword-Free Clinical Stimulus Battery for Mechanistic Interpretability of Emotion in Language Models
AIPsy-Affect supplies 480 keyword-free clinical vignettes and matched neutral controls for mechanistic interpretability studies of emotion in language models.
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SUMMIR: A Hallucination-Aware Framework for Ranking Sports Insights from LLMs
SUMMIR is a multimetric ranking model that orders LLM-generated sports insights by importance while incorporating hallucination detection to improve factual reliability across cricket, soccer, basketball, and baseball articles.
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Dual-Perspective Disentangled Multi-Intent Alignment for Enhanced Collaborative Filtering
DMICF models interactions from user- and item-centric perspectives with a macro-micro prototype-aware variational encoder and dimension-wise intent alignment to improve collaborative filtering.