Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction

TL;DR

This large-scale study analyzes over 100,000 user reviews to empirically assess how ethical AI principles influence user satisfaction, revealing that ethical considerations are positively linked to satisfaction and vary across user types and product categories.

Contribution

The paper provides the first large-scale empirical evidence connecting ethical AI principles with user satisfaction, highlighting contextual differences across user roles and product types.

Findings

All seven ethical dimensions are positively associated with user satisfaction.

The strength of this association varies systematically across user and product types.

Technical users focus more on system-level concerns, while non-technical users emphasize human-centric aspects.

Abstract

As AI systems become increasingly embedded in organizational workflows and consumer applications, ethical principles such as fairness, transparency, and robustness have been widely endorsed in policy and industry guidelines. However, there is still scarce empirical evidence on whether these principles are recognized, valued, or impactful from the perspective of users. This study investigates the link between ethical AI and user satisfaction by analyzing over 100,000 user reviews of AI products from G2. Using transformer-based language models, we measure sentiment across seven ethical dimensions defined by the EU Ethics Guidelines for Trustworthy AI. Our findings show that all seven dimensions are positively associated with user satisfaction. Yet, this relationship varies systematically across user and product types. Technical users and reviewers of AI development platforms more frequently discuss system-level concerns (e.g., transparency, data governance), while non-technical users and reviewers of end-user applications emphasize human-centric dimensions (e.g., human agency, societal well-being). Moreover, the association between ethical AI and user satisfaction is significantly stronger for non-technical users and end-user applications across all dimensions. Our results highlight the importance of ethical AI design from users' perspectives and underscore the need to account for contextual differences across user roles and product types.