Between Trust and Risk: Understanding the Conditional Acceptance of Artificial Intelligence

Artificial Intelligence (AI) is rapidly transitioning from a specialized technology to an everyday socio-technical infrastructure, yet public acceptance remains shaped by a trade-off between perceived benefits and risks. This study examines how individuals from varied demographic and professional backgrounds perceive, use, and evaluate AI-enabled systems using a mixed-method research design. A bilingual (English/Arabic) online survey (N=115) captured demographics, awareness, usage patterns, perceived impact, self-assessed understanding, domain-specific trust, concerns, and attitudes toward regulation, complemented by open-ended reflections. In parallel, semi-structured face-to-face interviews provided deeper insight into AI conceptualization, lived experiences, trust boundaries, and conditions for acceptable use. Quantitative results show frequent AI engagement embedded in daily life, with strong domain dependence in trust: education is the most trusted domain, whereas healthcare and finance attract substantially lower trust. Prominent concerns include overreliance (“brain rot”), privacy and data misuse, job displacement, and misinformation. Support for stronger AI regulation is high, indicating that governance is viewed as a prerequisite for sustainable adoption rather than a constraint on innovation. Qualitative findings triangulate these results, revealing a pattern of conditional acceptanceunderstood as the simultaneous valuation of AI’s practical utility alongside the imposition of explicit trust prerequisites whereby participants value AI for productivity and learning support while emphasizing confidentiality, transparency, human oversight in high-stakes contexts, and clear boundaries to mitigate misuse and erosion of human judgment. The study offers empirically grounded insights for policymakers, educators, and industry stakeholders into how AI acceptance is negotiated through utility, literacy, perceived risk, and expectations of accountability.