"AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI
September 28, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Agnes M. Kloft, Robin Welsch, Thomas Kosch, Steeven Villa
arXiv ID
2309.16606
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI
Citations
32
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
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
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