Structural transparency of societal AI alignment through Institutional Logics

2026-02-09

AI Summary

This study addresses a critical gap in AI alignment transparency research, which has predominantly focused on informational aspects while overlooking how institutional and organizational forces shape alignment decisions and their societal implications. The paper introduces a novel “structural transparency” framework, integrating institutional logics theory into AI alignment for the first time. It develops a macro-level analytical system encompassing institutional logic identification, analysis of external perturbations, and mapping of structural risks. Operationalized through a taxonomy and a five-component analytical model—implemented via an “analyst recipe”—the framework offers a practical toolkit that meaningfully complements existing information-centric transparency approaches. This enables systematic assessment of institutional dynamics and sociotechnical risks inherent in AI alignment governance.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

The field of AI alignment is increasingly concerned with the questions of how values are integrated into the design of generative AI systems and how their integration shapes the social consequences of AI. However, existing transparency frameworks focus on the informational aspects of AI models, data, and procedures, while the institutional and organizational forces that shape alignment decisions and their downstream effects remain underexamined in both research and practice. To address this gap, we develop a framework of \emph{structural transparency} for analyzing organizational and institutional decisions concerning AI alignment, drawing on the theoretical lens of Institutional Logics. We develop a categorization of organizational decisions that are present in the governance of AI alignment, and provide an explicit analytical approach to examining them. We operationalize the framework through five analytical components, each with an accompanying"analyst recipe"that collectively identify the primary institutional logics and their internal relationships, external disruptions to existing social orders, and finally, how the structural risks of each institutional logic are mapped to a catalogue of sociotechnical harms. The proposed concept of structural transparency enables analysts to complement existing approached based on informational transparency with macro-level analyses that capture the institutional dynamics and consequences of decisions regarding AI alignment.

Problem

• AI alignment
• structural transparency
• institutional logics
• organizational decisions
• sociotechnical harms

Innovation

• structural transparency
• AI alignment
• institutional logics
• sociotechnical harms
• organizational governance