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
CSETv1_Annotator-1, GMF

CSETv1_Annotator-1 Taxonomy Classifications

Taxonomy Details

Incident Number

168

Special Interest Intangible Harm

no

Notes (AI special interest intangible harm)

5.4 - Users of deployers with little interest in popular items

Date of Incident Year

2022

Date of Incident Month

03

Date of Incident Day

01

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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