A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Side-by-side comparison of intent-equivalent SAE and AAVE tweets significantly exacerbates covert dialect bias in LMs compared to isolated evaluation, with explicit dialect labels worsening the effect further.
citing papers explorer
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Side-by-side Comparison Amplifies Dialect Bias in Language Models
Side-by-side comparison of intent-equivalent SAE and AAVE tweets significantly exacerbates covert dialect bias in LMs compared to isolated evaluation, with explicit dialect labels worsening the effect further.