AI incident ·
AI incident: sexual content (Jan 2025)
On this page 8 sections
In brief
An AI system built by Deepfake Technology Developers and deployed by Stephen Kamnik allegedly harmed Victims Whose Images Were Manipulated and Pennsylvania State Police.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 7 reports
What happened
A Pennsylvania State Police corporal, Stephen Kamnik, was charged for allegedly using a work computer to store thousands of pornographic files, including content created with deepfake AI software.
Laws on record for this risk area
Policy angle: Classified under Discrimination and Toxicity (Exposure to toxic content) in the MIT AI Risk Repository taxonomy; 4 instruments on record cover this risk area.
- India DPDP Act
- Law No. 132/2025 on artificial intelligence
- NYC Local Law 144 (automated employment decision tools)
- EU AI Act
Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (7)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Stephen Kamnik
- Alleged developer
- Deepfake Technology Developers
- Alleged harmed party
- Victims Whose Images Were Manipulated, Pennsylvania State Police
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.2 Exposure to toxic content
- Causal entity
- Human
- Intent
- Intentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 1.2.
- Harmful Content
"The LLM-generated content sometimes contains biased, toxic, and private information"
- Toxicity
"Toxicity means the generated content contains rude, disrespectful, and even illegal information"
- Toxic Training Data
"Following previous studies [96], [97], toxic data in LLMs is defined as rude, disrespectful, or unreasonable language that is opposite to a polite, positive, and healthy language environment, including hate speech, offe...
- Not-Suitable-for-Work (NSFW) Prompts
"Inputting a prompt contain an unsafe topic (e.g., notsuitable-for-work (NSFW) content) by a benign user. "
- Toxicity and Abusive Content
This typically refers to rude, harmful, or inappropriate expressions.
- Controversial Opinions
The controversial views expressed by large models are also a widely discussed concern. Bang et al. (2021) evaluated several large models and found that they occasionally express inappropriate or extremist views when disc...
- Harmful responses
"Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is n...
- Violation of social norms
"Second, because LLMs are trained on internet text data, there is also a risk that model weights encode functions which, if deployed in particular contexts, would violate social norms of that context. Following the princ...
Incidents in the same risk subdomain
- KBS AI Translation Subtitles Reportedly Broadcast Profanity During Artemis II Launch Livestream
- Grok Allegedly Generated Publicly Visible Sexist Abuse Targeting Swiss Finance Minister Karin Keller-Sutter After X User Prompt
- Trump Reportedly Posted Purportedly AI-Generated Racist Video Depicting Barack and Michelle Obama as Apes on Truth Social
- Tencent's WeChat-Integrated Yuanbao Chatbot Reportedly Insulted User During Coding Debug Request
- Alleged Harmful Outputs and Data Exposure in Children's AI Products by FoloToy, Miko, and Character.AI
- AI Training Dataset for Detecting Nudity Allegedly Found to Contain CSAM Images of Identified Victims
Source record: incident #893 on the AI Incident Database · all 7 reports