This study explores the risk preferences of Large Language Models (LLMs) and how the process of aligning them with human ethical standards influences their economic decision-making. By analyzing 30 LLMs, we uncover a broad range of inherent risk profiles ranging from risk-averse to risk-seeking. We then explore how different types of AI alignment, a process that ensures models act according to human values and that focuses on harmlessness, helpfulness, and honesty, alter these base risk preferences. Alignment significantly shifts LLMs towards risk aversion, with models that incorporate all three ethical dimensions exhibiting the most conservative investment behavior. Replicating a prior study that used LLMs to predict corporate investments from company earnings call transcripts, we demonstrate that although some alignment can improve the accuracy of investment forecasts, excessive alignment results in overly cautious predictions. These findings suggest that deploying excessively aligned LLMs in financial decision-making could lead to severe underinvestment. We underline the need for a nuanced approach that carefully balances the degree of ethical alignment with the specific requirements of economic domains when leveraging LLMs within finance.

本研究探讨了大型语言模型（LLMs）的风险偏好，以及将它们与人类伦理标准对齐的过程如何影响它们的经济决策。通过分析30个LLM，我们发现了从风险规避到风险追求的广泛固有风险特征。接着，我们探讨了不同类型的AI对齐如何改变这些基础风险偏好，发现对齐显著将LLMs转向风险规避，同时融合三个伦理维度的模型表现出最保守的投资行为。通过复制一项先前的研究，该研究使用LLMs根据公司收益电话会议记录预测公司投资，我们证明了尽管一些对齐可以提高投资预测的准确性，但过度对齐会导致过于谨慎的预测。这些发现表明，在金融决策中部署过度对齐的LLMs可能会导致严重的低投资情况。因此，在金融中应使用一种细腻的方法，平衡伦理对齐的程度与经济领域的具体要求。

人工智能应该有多少道德？人工智能对低水平智能风险偏好的影响