Sensitive prompts serve as an early-warning signal for fairness risks in LLMs by eliciting responses that often miss ethical or contextual implications.
Fairness percep- tions of algorithmic decision-making: A systematic review of the empiri- cal literature,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Bias Ahead: Sensitive Prompts as Early Warnings for Fairness in Large Language Models
Sensitive prompts serve as an early-warning signal for fairness risks in LLMs by eliciting responses that often miss ethical or contextual implications.