Bottom-Up Perspectives on AI Governance: Insights from User Reviews of AI Products

TL;DR

This study analyzes over 100,000 user reviews of AI products to uncover practical governance concerns expressed by users, revealing both well-known and overlooked issues across technical and organizational domains.

Contribution

It introduces a bottom-up, data-driven approach to understanding AI governance by extracting themes from user discourse, complementing normative frameworks with real-world insights.

Findings

Identifies governance-relevant themes in user reviews across technical and non-technical areas.

Highlights overlooked governance issues like project management and customer interaction.

Shows overlap with existing ethics frameworks on privacy and transparency.

Abstract

With the growing importance of AI governance, numerous high-level frameworks and principles have been articulated by policymakers, institutions, and expert communities to guide the development and application of AI. While such frameworks offer valuable normative orientation, they may not fully capture the practical concerns of those who interact with AI systems in organizational and operational contexts. To address this gap, this study adopts a bottom-up approach to explore how governance-relevant themes are expressed in user discourse. Drawing on over 100,000 user reviews of AI products from G2.com, we apply BERTopic to extract latent themes and identify those most semantically related to AI governance. The analysis reveals a diverse set of governance-relevant topics spanning both technical and non-technical domains. These include concerns across organizational processes-such as planning, coordination, and communication-as well as stages of the AI value chain, including deployment infrastructure, data handling, and analytics. The findings show considerable overlap with institutional AI governance and ethics frameworks on issues like privacy and transparency, but also surface overlooked areas such as project management, strategy development, and customer interaction. This highlights the need for more empirically grounded, user-centered approaches to AI governance-approaches that complement normative models by capturing how governance unfolds in applied settings. By foregrounding how governance is enacted in practice, this study contributes to more inclusive and operationally grounded approaches to AI governance and digital policy.