# "AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI

arXiv: https://arxiv.org/abs/2309.16606 · HTML: https://mlsanity.com/paper/2309.16606/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 123 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions](https://mlsanity.com/paper/2107.07015/) (2021)
- [Toward a principled Bayesian workflow in cognitive science](https://mlsanity.com/paper/1904.12765/) (2019)
- [MAILS -- Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies](https://mlsanity.com/paper/2302.09319/) (2023)
- [Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods](https://mlsanity.com/paper/2205.00189/) (2022)
- [The Placebo Effect of Artificial Intelligence in Human-Computer Interaction](https://mlsanity.com/paper/2204.04935/) (2022)
- [P-value: A Bless or A Curse for Evidence-Based Studies?](https://mlsanity.com/paper/1809.08503/) (2018)
- [Short-Form Videos Degrade Our Capacity to Retain Intentions: Effect of Context Switching On Prospective Memory](https://mlsanity.com/paper/2302.03714/) (2023)
- [Fears about AI-mediated communication are grounded in different expectations for one's own versus others' use](https://mlsanity.com/paper/2305.01670/) (2023)

## Built on by (most cited first, 11 of 11)

- [Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences](https://mlsanity.com/paper/2404.12272/) (2024)
- [Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions](https://mlsanity.com/paper/2502.03804/) (2025)
- [Mental Models of Autonomy and Sentience Shape Reactions to AI](https://mlsanity.com/paper/2512.09085/) (2025)
- [Large Language Models to the Rescue: Reducing the Complexity in Scientific Workflow Development Using ChatGPT](https://mlsanity.com/paper/2311.01825/) (2023)
- [Position: Stop Making Unscientific AGI Performance Claims](https://mlsanity.com/paper/2402.03962/) (2024)
- [Content-Driven Local Response: Supporting Sentence-Level and Message-Level Mobile Email Replies With and Without AI](https://mlsanity.com/paper/2502.06430/) (2025)
- [From Sound to Sight: Towards AI-authored Music Videos](https://mlsanity.com/paper/2509.00029/) (2025)
- [HappyRouting: Learning Emotion-Aware Route Trajectories for Scalable In-The-Wild Navigation](https://mlsanity.com/paper/2401.15695/) (2024)
- [Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks](https://mlsanity.com/paper/2410.13326/) (2024)
- [AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law](https://mlsanity.com/paper/2605.00582/) (2026)
- [Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Work](https://mlsanity.com/paper/2609.17065/) (2026)

## Abstract (arXiv)

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