AI incident ·
Alleged AI-Generated Photo Alteration Leads to Inappropriate Modifications in Speaker's Conference Picture
On this page 8 sections
In brief
An AI system built by Unknown Developer and deployed by Unknown Conference Employee allegedly harmed Elizabeth Laraki.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 5 reports
What happened
An AI image expansion tool used by a conference organizer unintentionally altered Elizabeth Laraki’s profile picture for a marketing ad, making her blouse appear unbuttoned and showing a fabricated hint of undergarments. The AI generated the lower part of the image when expanding it for vertical formatting. The conference organizers quickly apologized and removed the altered content.
Laws on record for this risk area
Policy angle: Classified under Discrimination and Toxicity (Unfair discrimination and misrepresentation) 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 (5)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Unknown Conference Employee
- Alleged developer
- Unknown Developer
- Alleged harmed party
- Elizabeth Laraki
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...
- 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...
- 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."
- 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
- Algorithmic Bias in French Welfare System Allegedly Discriminates Against Marginalized Groups
- Department for Work and Pensions (DWP) AI Systems Allegedly Discriminate Against Single Mothers
- VA Suicide Prevention Algorithm REACH VET Reportedly Prioritizes Men Over Women Veterans
Source record: incident #820 on the AI Incident Database · all 5 reports