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.

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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

1.1. Unfair discrimination and misrepresentation

Risk Domain

  1. Discrimination and Toxicity

Entity

AI

Timing

Post-deployment

Intent

Intentional

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.
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