Skip to Content
logologo
AI Incident Database
Open TwitterOpen RSS FeedOpen FacebookOpen LinkedInOpen GitHub
Open Menu
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse

Incident 206: Tinder's Personalized Pricing Algorithm Found to Offer Higher Prices for Older Users

Description: Tinder’s personalized pricing was found by Consumers International to consider age as a major determinant of pricing, and could be considered a direct discrimination based on age, according to anti-discrimination law experts.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: Tinder developed and deployed an AI system, which harmed Tinder users over 30 years old.

Incident Stats

Incident ID
206
Report Count
4
Incident Date
2015-03-01
Editors
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
 

1.1. Unfair discrimination and misrepresentation

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
 

Intentional

Incident Reports

Reports Timeline

Incident OccurrenceTinder Plus pricing higher for older people+2
Tinder's unfair pricing algorithm exposed
Tinder Plus pricing higher for older people

Tinder Plus pricing higher for older people

choice.com.au

Tinder's unfair pricing algorithm exposed

Tinder's unfair pricing algorithm exposed

which.co.uk

Over thirty? You could be paying more to swipe right

Over thirty? You could be paying more to swipe right

consumer.org.nz

A Consumer Investigation into Personalised Pricing

A Consumer Investigation into Personalised Pricing

consumersinternational.org

Tinder Plus pricing higher for older people
choice.com.au · 2020

Allan Candelore had a problem with Tinder Plus prices, and made it known. He sued the company behind the dating app for discrimination. It was 2015.

In a California trial court, Tinder argued there was no problem. The company was charging u…

Tinder's unfair pricing algorithm exposed
which.co.uk · 2022

Over-30s pay more for Tinder Plus, Which? finds

A Which? investigation has revealed that market-leading dating app Tinder routinely charges over-30s more for its 'Plus' subscription.

UPDATE: Our original analysis also found that Tinder appe…

Over thirty? You could be paying more to swipe right
consumer.org.nz · 2022

New research from Consumers International and the Mozilla Foundation has found that Tinder Plus users over thirty are paying 65 percent more than their younger counterparts on average to swipe right. The research looked at pricing from New …

A Consumer Investigation into Personalised Pricing
consumersinternational.org · 2022

Background to Case Study

The primary objective of consumer organisations is the protection and promotion of consumer rights. Where possible, consumer organisations seek to combine efforts across countries to partner, leverage, and learn tog…

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.
Previous IncidentNext Incident

Research

  • Defining an “AI Incident”
  • Defining an “AI Incident Response”
  • Database Roadmap
  • Related Work
  • Download Complete Database

Project and Community

  • About
  • Contact and Follow
  • Apps and Summaries
  • Editor’s Guide

Incidents

  • All Incidents in List Form
  • Flagged Incidents
  • Submission Queue
  • Classifications View
  • Taxonomies

2024 - AI Incident Database

  • Terms of use
  • Privacy Policy
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • 300d90c