Description: An AI-powered website by Washington State's Lottery is reported to have inadvertently produced a softcore pornographic image of a user, leading to the site’s immediate shutdown out of caution.
Entities
View all entitiesAlleged: developed and deployed an AI system, which harmed .
Incident Stats
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.2. Exposure to toxic content
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Discrimination and Toxicity
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
Incident Reports
Reports Timeline
A new Washington's Lottery AI mobile site turned a user's photo into softcore pornography, forcing them to take the website down "out of an abundance of caution."
When Megan, a 50-year-old mother based in Tumwater, visited the new AI-powere…
A user of the Washington Lottery's "Test Drive a Win" website says it used AI to generate (the unredacted version of) this image with her face on a topless body.
The Washington State Lottery has taken down a promotional AI-powered web app a…
Variants
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.