Google Photos labels Black individuals as gorillas
In 2015, Google's Photos app incorrectly tagged images of Black people as gorillas. The company apologized for the failure and took steps to prevent the specific label from appearing.
Algorithmic bias in training data led a computer vision model to misclassify Black faces as primates.
Key facts
- What
- In 2015, Google's Photos app incorrectly tagged images of Black people as gorillas.
- Incident date
- Jul 1, 2015
- Who
- Failure mode
- Brand & Safety Incident
- AI surface
- Computer Vision
- Severity
- High
What happened
In July 2015, users reported that Google Photos' auto-tagging feature misidentified images of Black individuals as gorillas. Google issued a public apology, stating they were appalled by the result. The company immediately took action to prevent this specific classification from occurring.
What broke inside the model
- 01 · TriggerA user prompts the model in public view.
- 02 · Model stepThe model produces unsafe or off-brand output.
- 03 · Control gapNo filter holds the line before publish.
- 04 · FailureThe output goes public unchecked.
- 05 · ConsequenceA reputational or safety incident lands.
A contained signal crosses into output that goes public.
The computer vision model suffered from algorithmic bias due to an insufficient diversity of skin tones in its training data. This caused the image recognition system to misclassify Black human faces as primates.
What it cost
Sources
- PressGoogle apologises for Photos app's racist blunderbbc.com
- PressGoogle apologizes for racist auto-tag in photo appphillyvoice.com
Cite this entry
https://failureindex.ai/failures/google-photos-labels-black-individuals-gorillasAI Failure Index. "Google Photos labels Black individuals as gorillas" (FI-0353). Realm Labs. https://failureindex.ai/failures/google-photos-labels-black-individuals-gorillas (indexed Jun 9, 2026).Data fields CC-BY 4.0, prose citation permitted. Incident ID FI-0353. Full dataset at /data.
Note from Realm Labs, the Index steward
How Realm fits
- Prism
- OmniGuard
- AI Detection & Response (AIDR)
This entry sits in the index's predictive wing: a system that scores, ranks, perceives, or steers rather than generates. Realm's runtime layer is built for the generative and agentic systems now moving into these same decision seats, where it watches a model's internal state and holds an unsupported claim or an unchecked action before it commits. The control gap on this record, an automated decision that reached people with no runtime check in front of it, is the same gap. The index keeps predictive failures on the record because the pattern carries straight into the systems shipping today.