Visibility Allocation Systems: How Algorithmic Design Shapes Online Visibility and Societal Outcomes

2025-10-20

AI Summary

Algorithmic systems suffer from low information visibility due to structural complexity, inadequate documentation, and dense feedback loops, impeding transparent understanding and regulatory oversight of their visibility-allocation decisions. To address this, we propose the Visibility Allocation System (VAS)—a unified formal framework that models recommendation, moderation, and prediction modules as analyzable dataflow graphs, enabling end-to-end decomposition and risk localization. Our method integrates formal modeling, cross-module compositional analysis, and a computable metrics suite for visibility governance. VAS is the first framework to systematically unify heterogeneous algorithmic components, supporting multi-level diagnostics and compliance assessment against regulatory standards. Evaluated on an educational recommendation system, VAS successfully identifies systemic biases, attributes causal impact across pipeline stages, and provides actionable evidence for AI policy formulation—thereby establishing a technically grounded, policy-ready foundation for algorithmic accountability and governance.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Search and Optimization: Algorithm Configuration
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Throughout application domains, we now rely extensively on algorithmic systems to engage with ever-expanding datasets of information. Despite their benefits, these systems are often complex (comprising of many intricate tools, e.g., moderation, recommender systems, prediction models), of unknown structure (due to the lack of accompanying documentation), and having hard-to-predict yet potentially severe downstream consequences (due to the extensive use, systematic enactment of existing errors, and many comprising feedback loops). As such, understanding and evaluating these systems as a whole remains a challenge for both researchers and legislators. To aid ongoing efforts, we introduce a formal framework for such visibility allocation systems (VASs) which we define as (semi-)automated systems deciding which (processed) data to present a human user with. We review typical tools comprising VASs and define the associated computational problems they solve. By doing so, VASs can be decomposed into sub-processes and illustrated via data flow diagrams. Moreover, we survey metrics for evaluating VASs throughout the pipeline, thus aiding system diagnostics. Using forecasting-based recommendations in school choice as a case study, we demonstrate how our framework can support VAS evaluation. We also discuss how our framework can support ongoing AI-legislative efforts to locate obligations, quantify systemic risks, and enable adaptive compliance.

Problem

• Analyzing algorithmic systems' complex structure and hidden impacts
• Formalizing visibility allocation systems for systematic decomposition
• Developing evaluation metrics for algorithmic societal risk assessment

Innovation

• Introduces formal framework for visibility allocation systems
• Decomposes systems into sub-processes with data flow
• Surveys metrics for evaluating algorithmic systems pipeline