有偏见的AI提升人类表现但降低感知帮助度
Biased AI improves human performance but reduces perceived helpfulness
AI总结：
本研究通过三项涉及5000名参与者的随机实验发现，故意注入偏见的AI能提升人类在错误信息评估、金融投资等任务中的表现，但会降低感知帮助度，揭示AI设计中故意偏见的战略价值。
摘要：
人工智能（AI）日益塑造人们思考、参与和评估信息的方式。为将风险降至最低，当前大多数系统被设计为呈现意识形态中立且输出标准化。然而，越来越多的证据表明，这些原则抑制了认知参与，损害了人类决策，并侵蚀了社会多样性。在此，我们测试了相反的方法，即故意将偏见注入AI助手。在涉及5,000名参与者的三项随机实验中，相对于默认和中立的AI，有偏见的AI在从错误信息评估、金融投资到研究生教育等任务中提升了人类表现。这些收益伴随着主观代价。参与者系统性地低估了他们认为有偏见的AI，并夸大了他们认为中立的AI的帮助度，而无论系统的实际行为如何。与两个其偏见位于参与者自身观点两侧的AI互动，保留了表现收益，同时限制了主观代价和单方面影响。我们的发现揭示了AI设计中故意偏见的战略价值。AI不是扮演一个单一、公平、可靠且权威的声音，而是从特定视角发言，触发认知能动性，并在判断、决策和问题解决中提升人机表现。
英文摘要：
Artificial intelligence (AI) increasingly shapes how people think, engage, and evaluate information. To minimize risk, most current systems are designed to present as ideologically neutral with standardized output. Yet growing evidence suggests that these principles suppress cognitive engagement, impair human decision-making, and erode societal diversity. Here we test the opposite approach by deliberately injecting bias into AI assistants. In three randomized experiments with 5,000 participants, biased AI improved human performance relative to default and neutral AI in tasks ranging from misinformation evaluation and financial investment to graduate education. These gains carried a subjective cost. Participants systematically undervalued AI they believed to be biased and inflated the helpfulness of AI they believed to be neutral, regardless of the systems' actual behavior. Interacting with two AIs whose biases flanked the participant's own perspective preserved the performance gains while limiting the subjective cost and one-sided influence. Our findings reveal the strategic value of intentional bias in AI design. Rather than performing a single fair, reliable, and authoritative voice, AI that speaks from specific viewpoints triggers cognitive agency and elevates human-AI performance in judgment, decision-making, and problem-solving.
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。
