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AI incident #168 ·

Collaborative Filtering Prone to Popularity Bias, Resulting in Overrepresentation of Popular Items in the Recommendation Outputs

Open on the AI Incident Database 2 news reports Snapshot 7 Sep 2026

What happened

Collaborative filtering prone to popularity bias, resulting in overrepresentation of popular items in the recommendation outputs.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (2)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
    arxiv.org · Dominik Kowald, Emanuel Lacic · AIID #1541
  2. Why AI Isn’t Providing Better Product Recommendations
    unite.ai · Martin Anderson · AIID #1540

Who was involved

Alleged harmed party
Facebook Users, Linkedin Users, Youtube Users, Netflix Users, X (Twitter) Users

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 1.3.

  • Bias and discrimination (value embedding)

    "Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

  • Impact on affected communities

    "It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the r...

    AI Risk Atlas (IBM2025)

  • Unfair capability distribution

    "Performing worse for some groups than others in a way that harms the worse-off group"

    A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025)

  • Disparate Performance

    The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups...

    Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  • Fairness

    Avoiding bias and ensuring no disparate performance

    Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

  • Ideological Homogenization from Value Embedding

    "The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world c...

    Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse and Systemic Risks (Maham2023 )

  • Fairness

    This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved o...

    A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions (Saghiri2022)

  • Quality-of-Service Harms

    "These harms occur when algorithmic systems disproportionately underperform for certain groups of people along social categories of difference such as disability, ethnicity, gender identity, and race."

    Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023)

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