The User-first Approach to AI Ethics: Preferences for Ethical Principles in AI Systems across Cultures and Contexts

TL;DR

This study empirically investigates user preferences for AI ethical principles across different cultures and contexts, revealing significant variation and highlighting the importance of a user-centered approach to AI ethics design.

Contribution

It provides the first large-scale empirical data on user priorities for AI ethics principles across diverse cultural and contextual settings.

Findings

Users prioritize privacy, justice & fairness, and transparency.

Preferences vary significantly by culture and application context.

Four distinct user cohorts identified, including an ethically disengaged group.

Abstract

As AI systems increasingly permeate everyday life, designers and developers face mounting pressure to balance innovation with ethical design choices. To date, the operationalisation of AI ethics has predominantly depended on frameworks that prescribe which ethical principles should be embedded within AI systems. However, the extent to which users value these principles remains largely unexplored in the existing literature. In a discrete choice experiment conducted in four countries, we quantify user preferences for 11 ethical principles. Our findings indicate that, while users generally prioritise privacy, justice & fairness, and transparency, their preferences exhibit significant variation based on culture and application context. Latent class analysis further revealed four distinct user cohorts, the largest of which is ethically disengaged and defers to regulatory oversight. Our findings offer (1) empirical evidence of uneven user prioritisation of AI ethics principles, (2) actionable guidance for operationalising ethics tailored to culture and context, (3) support for the development of robust regulatory mechanisms, and (4) a foundation for advancing a user-centred approach to AI ethics, motivated independently from abstract moral theory.