2025/10/11 by Lior Gazit, Ofer Arazy, Uri Hertz · 1 voice
Neuroscience · Social Sciences · #Experimental Behavioral Economics Studies #Psychology of Moral and Emotional Judgment #Social and Intergroup Psychology
paper · doi:10.1016/j.chbah.2025.100218
openalex created_date 2025/10/11 · openalex publication_date 2025/10/11 · openalex updated_date 2026/07/23
As technology companies develop AI agents designed to function as friends, therapists, and personal advisors, a fundamental question arises: can algorithms fulfill these intimate social roles? Relational Models Theory (RMT) suggests that relationships shape normative expectations in social decisions. Our research examines the perceived relationship between human/algorithmic advisors and advisee. Across two experiments (N = 492), participants reported their expectations from advisors that recommended splitting money between the advisee and an unknown other. Participants expected algorithmic advisors to exhibit higher consistency and higher sensitivity to others' payoffs, even when this resulted in smaller gains for the advisee, reflecting expectations of institutional fairness rather than personal favoritism. In contrast, participants anticipated that human advisors would prioritize their own welfare, consistent with personal relational norms. Seeking to validate that relational norms indeed drive expectations, in a follow-up experiment, we framed advisors as either "Institutional" or "Personal". Participants expected both human and algorithmic advisors to show higher sensitivity to others' payoffs and greater consistency when framed as Institutional, in line with RMT. However, regardless of framing, participants expected algorithmic advisors to exhibit higher sensitivity to others’ payoffs and greater consistency than the expectations from human advisors. Our findings extend Human-AI interaction literature by showing that people apply different normative standards to algorithmic versus human advisors. Results suggest that while relational framing can influence perceptions, attempts to position AI as replacements for humans must account for the persistent tendency to view algorithms through an institutional lens. • When seeking advice in social dilemmas, relationships shape advisees' expectations, such that they expect friends to advise differently than strangers. This study systematically examines how relational factors differently influence expectations from algorithmic versus human advisors • In social dilemmas experiments, participants expected algorithmic advisors to show higher sensitivity to others’ payoffs (STOP) and greater consistency in their advice compared to perceptions from human advisors. • When framed as institutional representatives, both human and algorithmic advisors were expected to advise with higher sensitivity to others’ payoffs (STOP) and greater consistency, compared to when framed as personal advisors. • Regardless of framing, algorithmic advisors were expected to show higher sensitivity to others’ payoffs (STOP) and greater consistency, when compared to the expectations from human advisors, suggesting that people persistently view AI through an institutional relationship lens.