CONCEPT

Placebic Information

Langer's 1978 finding that reasons with the structure of reasons—but no actual content—produce compliance comparable to genuine reasons; now applied to AI explanations that look like understanding without providing it.

In 1978, Langer and colleagues approached people waiting to use a photocopier and asked to cut in line, varying the request in three conditions. Real reason: "May I use the machine, because I'm in a rush?" No reason: "May I use the machine?" Placebic reason: "May I use the machine, because I need to make copies?" The placebic condition is the revealing one. "Because I need to make copies" explains nothing—everyone waiting to use a copier needs to make copies. Compliance in the placebic condition was nearly as high as in the real-reason condition, and significantly higher than in the no-reason condition. The structure of a reason—the word "because" followed by words—was sufficient. The content of the reason was irrelevant.

In The You On AI Field Guide

The finding has migrated directly into AI research. A 2019 study at the ACM Conference on Human Factors in Computing Systems investigated whether placebic explanations of AI decisions would produce trust comparable to genuine explanations. Users rated

In The You On AI Field Guide

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