People of Tinder dataset
People of Tinder is a dataset created in 2016 by software engineer Stuart Colianni that was intended 'to build a better, larger facial dataset' capable of distinguishing between male and female images. The dataset comprised 40,000 images of people's faces - half women, half men - from the San Francisco area scraped from dating app Tinder. Dataset 🤖 Released: 2016 Developer: Stuart Colianni Purpose: Train neural networks Type: Database/dat aset T echnique : Computer vision; Facial recognition; M achine learning Transparency, accountability 🙈 The People of Tinder dataset has several transparency limitations: Lack of consent. The dataset was created by scraping Tinder profiles without obtaining consent from the users whose data was collected, raising significant ethical and privacy concerns. Data collection . The methods used to gather the data, including the specifics of the scraping process and the criteria for selecting profiles, are not fully transparent. Dataset creation. There is a lack of comprehensive documentation detailing the dataset's creation, including the exact data points collected and the potential biases introduced during the data collection process. Potential for bias. The dataset may not represent a diverse range of Tinder users, as it is unclear how profiles were selected and whether the dataset includes a balanced demographic representation. Restricted access. The dataset is not openly shared, limiting the ability of researchers to independently verify or analyse its contents and further reducing transparency. Risks, harms 🛑 The People of Tinder dataset raised serious ethical and privacy concerns by scraping and sharing personal photos and information from Tinder profiles without users' consent, potentially exposing individuals to harassment, identity theft, and the unintended analysis of their personal data and dating preferences. Incidents, issues 🔥 Tinder accuses dataset creator of violating terms of service
- Product, system or model
- People of Tinder
This incident was imported from AIAAIC and is used under CC BY-SA 4.0. Our additions to it — the structured fields, the translation, the checks against other reports — are published under the same licence.
This is a record of what was reported, not a finding that anyone broke the law. If it names your organisation and you believe it is wrong, the corrections process is free and open to everyone.