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Algorithm Delegation: How Embedded AI Facilitates Agency Transference in Medical Services

  • Rafael Wagner*
  • , Diego Costa Pinto
  • , Diogo Hildebrand
  • , Natália Araújo Pacheco
  • , Gurpreet Dhillon
  • , Marcia Maurer Herter
  • *Corresponding author for this work

Research output: Indexed journal article Articlepeer-review

6 Citations (Scopus)

Abstract

Integrating insights from psychological distance and human-AI agency theories, this study explores embedded AI — algorithms physically integrated into tangible devices as a novel mechanism to help overcome resistance towards medical AI. Across four studies, including a large text-mining (n = 224,433 reviews) and three controlled experiments (n = 677 participants), we show that embedded AI reduces psychological distance between consumers and AI (Study 2B), which in turn enhances willingness to delegate medical decisions to algorithms (Studies 1–3). We further demonstrate that consumers expect better health outcomes when delegating decisions to embedded AI (Studies 2 A, 2B, and 3). Moderation analysis further shows that these effects weaken when an analytical mode is primed using numerical accuracy information (Study 3). Our findings contribute to ongoing discussions on overcoming resistance to medical AI and offer implications for the design of AI-based technologies in medical services.

Original languageEnglish
Pages (from-to)2902-2921
Number of pages20
JournalPsychology and Marketing
Volume42
Issue number11
DOIs
Publication statusPublished - Nov 2025
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • artificial intelligence
  • delegation
  • embedded AI
  • human-AI agency
  • medical services
  • psychological distance

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