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

arXiv: https://arxiv.org/abs/2510.17241 · HTML: https://mlsanity.com/paper/2510.17241/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 139 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [A Long-Term Analysis of Polarization on Twitter](https://mlsanity.com/paper/1703.02769/) (2017)
- [Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems](https://mlsanity.com/paper/2008.13528/) (2020)
- [Filter List Generation for Underserved Regions](https://mlsanity.com/paper/1910.07303/) (2019)
- [An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial Homogamy](https://mlsanity.com/paper/2103.03332/) (2021)
- [15 Years of Algorithmic Fairness -- Scoping Review of Interdisciplinary Developments in the Field](https://mlsanity.com/paper/2408.01448/) (2024)

## Abstract (arXiv)

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.