Heightened AI expectations facilitate performance in human-AI interactions through placebo effects. While lowering expectations to control for placebo effects is advisable, overly negative expectations could induce nocebo effects. In a letter discrimination task, we informed participants that an AI would either increase or decrease their performance by adapting the interface, but in reality, no AI was present in any condition. A Bayesian analysis showed that participants had high expectations and performed descriptively better irrespective of the AI description when a sham-AI was present. Using cognitive modeling, we could trace this advantage back to participants gathering more information. A replication study verified that negative AI descriptions do not alter expectations, suggesting that performance expectations with AI are biased and robust to negative verbal descriptions. We discuss the impact of user expectations on AI interactions and evaluation and provide a behavioral placebo marker for human-AI interaction

翻译：“AI提升我们的表现，我毫不怀疑这个也会如此”：安慰剂效应对AI的负面描述具有鲁棒性 在人类与AI的交互中，高期望通过安慰剂效应提升表现。尽管降低期望以控制安慰剂效应是明智的，但过度负面的期望可能引发反安慰剂效应。在一项字母辨别任务中，我们告知参与者AI将通过调整界面提高或降低其表现，但实际上在任何条件下均不存在真正的AI。贝叶斯分析显示，无论AI描述是正面还是负面，当伪AI存在时，参与者均抱有高期望且描述性表现更优。通过认知建模，我们可将此优势追溯至参与者收集了更多信息。一项重复研究证实，负面AI描述不会改变期望，表明与AI相关的表现期望存在偏差且对负面口头描述具有鲁棒性。我们讨论了用户期望对AI交互及评估的影响，并提出了人类-AI交互的行为安慰剂标记。