Study finds co-designing AI agents drives user overtrust

A qualitative study published on arXiv indicates that co-designing large language model preference agents with users increases perceived accuracy despite objective misalignment. Researchers observed twelve participants designing household energy agents who subsequently rated the models as highly representative of their personal preferences. However, independent validation revealed agent responses were significantly more homogeneous, decisive, and abstract than actual human inputs. The authors argue that participation functions as an overtrust engine where process transparency conceals systematic alignment failures. This mechanism suggests individual alignment should be treated as an enacted process rather than a fixed state. The findings challenge assumptions that user involvement automatically guarantees ethical or accurate AI representation in preference modeling applications.