AI incident #1311 ·
Peppermill Casino Facial Recognition System Reportedly Misidentified Individual, Leading to Wrongful Arrest in Reno
What happened
Purported facial recognition technology deployed at Reno's Peppermill Casino reportedly misidentified Jason Killinger as an individual previously banned from the venue. Casino security reportedly detained Killinger and contacted police, leading to his arrest by the Reno PD. He was reportedly held for 11 hours, including several hours in handcuffs, before fingerprinting reportedly confirmed the misidentification. The incident resulted in alleged physical injury and civil liberties harms.
Editor's notes (AI Incident Database)
Timeline notes: The reported incident occurred 09/17/2023. Reporting on it began in earnest in November 2025. The incident ID was created 12/16/2025.
Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.
News reports (1)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Reno Police Department Peppermill Casino Law enforcement Facial recognition system deployers
- Alleged developer
- Facial recognition system developers
- Alleged harmed party
- People misidentified by facial recognition systems Jason Killinger
AI systems implicated
Facial recognition systemsAI-enabled decision support systems
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.1 Unfair discrimination and misrepresentation
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 1.1.
- Discrimination
"Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other pro...
- Harms of Representation and Other Biases
"A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce...
- Risks from bias and underrepresentation
"The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains su...
- Bias
"General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of hu...
- Bias
"The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"
- Toxicity and Bias Tendencies
"Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."
- Biased Training Data
"Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences...
- Broken systems
"These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or g...
Incidents in the same risk subdomain
- DOGE Reportedly Relied on Unvetted ChatGPT Outputs in Canceling National Endowment for the Humanities Grants
- Sora Video Generator Has Reportedly Been Creating Biased Human Representations Across Race, Gender, and Disability
- Meta AI Characters Allegedly Exhibited Racism, Fabricated Identities, and Exploited User Trust
- Alleged AI-Generated Photo Alteration Leads to Inappropriate Modifications in Speaker's Conference Picture
- Algorithmic Bias in French Welfare System Allegedly Discriminates Against Marginalized Groups
- Department for Work and Pensions (DWP) AI Systems Allegedly Discriminate Against Single Mothers