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Incident 168: Collaborative Filtering Prone to Popularity Bias, Resulting in Overrepresentation of Popular Items in the Recommendation Outputs

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

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Alleged: Facebook , LinkedIn , YouTube , Twitter and Netflix developed and deployed an AI system, which harmed Facebook users , LinkedIn users , YouTube users , Twitter Users and Netflix users.

Incident Stats

Incident ID
168
Report Count
2
Incident Date
2022-03-01
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
GMF, MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

1.3. Unequal performance across groups

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. Discrimination and Toxicity

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

+1
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Why AI Isn’t Providing Better Product Recommendations
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

arxiv.org

Why AI Isn’t Providing Better Product Recommendations

Why AI Isn’t Providing Better Product Recommendations

unite.ai

Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
arxiv.org · 2022

Introduction

Collaborative filtering (CF) is one of the most traditional but also most powerful concepts for calculating personalized recommendations [22] and is vastly used in the field of multimedia recommender systems (MMRS) [11]. Howeve…

Why AI Isn’t Providing Better Product Recommendations
unite.ai · 2022

If you’re interested in obscure things, there are two reasons why your searches for items and products are likely to be less related to your interests than those of your ‘mainstream’ peers; either you’re a monetization ‘edge case’ whose int…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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