# YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations

arXiv: https://arxiv.org/abs/2203.10666 · HTML: https://mlsanity.com/paper/2203.10666/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2022.

## Built on by (most cited first, 10 of 10)

- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations](https://mlsanity.com/paper/2408.03772/) (2024)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [Tree of Preferences for Diversified Recommendation](https://mlsanity.com/paper/2601.02386/) (2025)
- [Regulating AI: Applying insights from behavioural economics and psychology to the application of article 5 of the EU AI Act](https://mlsanity.com/paper/2308.02041/) (2023)
- [Apolitical Intelligence? Auditing Delphi's responses on controversial political issues in the US](https://mlsanity.com/paper/2306.13000/) (2023)
- [Generative Propaganda](https://mlsanity.com/paper/2509.19147/) (2025)
- [Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies](https://mlsanity.com/paper/2603.08049/) (2026)

## Abstract (arXiv)

Recommendations algorithms of social media platforms are often criticized for placing users in "rabbit holes" of (increasingly) ideologically biased content. Despite these concerns, prior evidence on this algorithmic radicalization is inconsistent. Furthermore, prior work lacks systematic interventions that reduce the potential ideological bias in recommendation algorithms. We conduct a systematic audit of YouTube's recommendation system using a hundred thousand sock puppets to determine the presence of ideological bias (i.e., are recommendations aligned with users' ideology), its magnitude (i.e., are users recommended an increasing number of videos aligned with their ideology), and radicalization (i.e., are the recommendations progressively more extreme). Furthermore, we design and evaluate a bottom-up intervention to minimize ideological bias in recommendations without relying on cooperation from YouTube. We find that YouTube's recommendations do direct users -- especially right-leaning users -- to ideologically biased and increasingly radical content on both homepages and in up-next recommendations. Our intervention effectively mitigates the observed bias, leading to more recommendations to ideologically neutral, diverse, and dissimilar content, yet debiasing is especially challenging for right-leaning users. Our systematic assessment shows that while YouTube recommendations lead to ideological bias, such bias can be mitigated through our intervention.