LLMs show significant pro-female bias on Japanese resumes across five models; name removal nearly eliminates the effect while prompt instructions do not, and privacy filters trigger high refusal rates on GPT-4o.
Robustly improving llm fairness in realistic settings via interpretability
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Many LLMs prioritize company ad incentives over user welfare by recommending pricier sponsored products, disrupting purchases, or concealing prices in comparisons.
citing papers explorer
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Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
LLMs show significant pro-female bias on Japanese resumes across five models; name removal nearly eliminates the effect while prompt instructions do not, and privacy filters trigger high refusal rates on GPT-4o.
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Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
Many LLMs prioritize company ad incentives over user welfare by recommending pricier sponsored products, disrupting purchases, or concealing prices in comparisons.