Incident 295: Wrongful Attempted Arrest for Apple Store Thefts Due to NYPD’s Facial Misidentification

Description: New York Police Department (NYPD)’s facial recognition system falsely connected a Black teenager to a series of thefts at Apple stores, which resulted in his wrongful attempted arrest.


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Alleged: unknown developed an AI system deployed by New York Police Department, which harmed Ousmane Bah , NYC Black people and NYC Black young people.

Incident Stats

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Khoa Lam
Teen sues Apple for $1 billion, blames facial recognition at stores for his arrest · 2019

An 18-year-old from New York is suing Apple for $1 billion, claiming that a facial recognition system at its stores falsely connected him to a series of thefts.

Ousmane Bah, who was arrested at his home in November, alleges that the warrant…

Apple claims it isn’t scanning customers’ faces, after teen sues for $1 billion · 2019

Apple is being accused of using facial recognition software in its Apple Stores to arrest the wrong person for theft — a New York student who’s now suing Apple for $1 billion. And while Apple tells The Verge it doesn’t use facial recognitio…

Judge allows facial recognition lawsuit against Apple to continue · 2020

An Illinois federal judge on Thursday refused to let Apple dodge a class action alleging that it violates state law through its facial recognition software, finding that the proposed class’ allegations that the company illegally collects an…

Apple sued over false accusations in Apple Store thefts by impostor · 2021

Apple is being sued by a man due to repeated accusations the man was behind numerous thefts at Apple Stores across the eastern seaboard of the United States, attempted thefts that were conducted by others who were impersonating the man.


Bah v. Apple Inc. · 2021

Because the allegations in the Third Amended Complaint at times differ materially from those considered by the Court in its first MTD Opinion, they will be described in some detail.

The first theft identified in the Complaint occurred on Ap…


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.