看见目标，却错过真相：人类对AI偏见的责任
Seeing the Goal, Missing the Truth: Human Accountability for AI Bias
AI总结：
本研究发现，人类设定的目标会影响LLM的行为，导致生成有偏见的指标，但这种偏见并非算法问题，而是源于人类在设计中确保AI测量的统计有效性
摘要：
本研究探讨了人类设定的目标如何影响大型语言模型（LLMs）的行为，通过目的条件认知。利用金融预测任务，我们显示，揭示LLM输出的下游用途（例如预测股票回报或收益）会导致LLM生成有偏见的情感和竞争指标，即使这些指标本应与下游任务无关。目标意识提示将中间指标转向揭示的下游目标。这种目的泄露在LLM知识截止前提高了性能，但截止后无优势。由于'看见目标'导致的AI偏见并非算法缺陷，而是源于人类在研究设计中确保AI生成测量的统计有效性和可靠性的责任。
英文摘要：
This research explores how human-defined goals influence the behavior of Large Language Models (LLMs) through purpose-conditioned cognition. Using financial prediction tasks, we show that revealing the downstream use (e.g., predicting stock returns or earnings) of LLM outputs leads the LLM to generate biased sentiment and competition measures, even though these measures are intended to be downstream task-independent. Goal-aware prompting shifts intermediate measures toward the disclosed downstream objective. This purpose leakage improves performance before the LLM's knowledge cutoff, but with no advantage post-cutoff. AI bias due to "seeing the goal" is not an algorithmic flaw, but stems from human accountability in research design to ensure the statistical validity and reliability of AI-generated measurements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
