My Advisor, Her AI, and Me: Evidence from a Field Experiment on Human–AI Collaboration and Investment Decisions
中文摘要
在关于是否应让人类保留在人工智能 (AI) 决策回路中的政策与管理层辩论持续进行之际,我们考察人类参与基于 AI 的服务生产是否会使下游消费者受益。我们与欧洲一家大型储蓄银行合作,生成了纯 AI 投资建议与人-AI 协作投资建议,将其提供给该银行客户,并在一项田野实验中考察其采纳建议的程度。在生产端,与人类可能低效地否决 AI 产出的担忧相反,我们的发现表明,让人类银行家处于基于 AI 的金融咨询回路之中、并赋予其对所提供建议的最终决定权,并不会损害建议质量。更重要的是,在消费端,我们发现与纯 AI 相比,银行客户更倾向于使其最终投资决策与人-AI 协作的建议保持一致,尤其是在面对风险更高的投资时。在我们的研究情境中,这种对人-AI 协作建议的更高依赖为消费者带来了更高的物质福利。来自田野实验的补充分析与一项在线控制实验表明,人-AI 协作建议的说服效果无法归因于消费者相信人类能力与 AI 能力之间的互补性提升了建议质量。相反,人类参与基于 AI 的服务所带来的消费端收益,主要源于人类参与充当了一种外围线索,从而增强了建议的情感吸引力。我们的发现表明,监管与指引应采取以消费者为中心的路径,通过营造人类能力与 AI 系统能够有效协同以惠及消费者的环境,同时保障消费者福利。这些细致的洞见,对于面临"提供纯 AI 服务还是人-AI 协作服务"决策的管理者,以及主张让人类保留在回路中的监管者而言,均至关重要。本文由 D. J. Wu 接收,属"人-算法连接"专刊。资助:本研究得到 Hi!PARIS 研究基金的支持。补充材料:在线附录与数据文件可在 https://doi.org/10.1287/mnsc.2022.03918 获取。
Abstract
Amid ongoing policy and managerial debates on keeping humans in the loop of artificial intelligence (AI) decision-making processes, we investigate whether human involvement in AI-based service production benefits downstream consumers. Partnering with a large savings bank in Europe, we produced pure AI and human–AI collaborative investment advice, which we passed to the bank customers and investigated the degree of their advice taking in a field experiment. On the production side, contrary to concerns that humans might inefficiently override AI output, our findings show that having a human banker in the loop of AI-based financial advisory by giving her the final say over the advice provided does not compromise the quality of the advice. More importantly, on the consumption side, we find that the bank customers are more likely to align their final investment decisions with advice from the human–AI collaboration, compared with pure AI, especially when facing more risky investments. In our setting, this increased reliance on human–AI collaborative advice leads to higher material welfare for consumers. Additional analyses from the field experiment along with an online controlled experiment indicate that the persuasive efficacy of human–AI collaborative advice cannot be attributed to consumers’ belief in increased advice quality resulting from complementarities between human and AI capabilities. Instead, the consumption-side benefits of human involvement in the AI-based service largely stem from human involvement serving as a peripheral cue that enhances the affective appeal of the advice. Our findings indicate that regulations and guidelines should adopt a consumer-centric approach by fostering environments where human capabilities and AI systems can synergize effectively to benefit consumers while safeguarding consumer welfare. These nuanced insights are crucial for managers who face decisions about offering pure AI versus human–AI collaborative services and also for regulators advocating for having humans in the loop. This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection. Funding: This work was supported by a Hi!PARIS fellowship. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03918 .