AI Training Dataset for Detecting Nudity Allegedly Found to Contain CSAM Images of Identified Victims
Description
An image dataset, NudeNet, used to train systems for detecting nudity was reportedly found to contain CSAM images, including material involving identified or known victims. According to the Canadian Centre for Child Protection, the dataset had been widely downloaded and cited in academic research prior to discovery. The images were allegedly included without vetting, exposing researchers to legal risk and perpetuating harm to victims. The dataset was subsequently removed following notification.
Actors
Alleged Deployers
- academic-researchers
- research-institutions
- ai-developers
- dataset-users
- independent-researchers
- ai-researchers
Alleged Developers
- nudenet-dataset-maintainers
- nudenet-model-developers
Harmed Parties
- minors
- academic-researchers
- people-depicted-in-non-consensual-intimate-imagery
- victims-of-child-sexual-abuse-material
Classification
Risk Domain
Discrimination & ToxicityRisk Subdomain
1.2. Exposure to toxic content
MIT Risk Profile
Topics
Source
Related Incidents (Discrimination & Toxicity)
View all →Research Papers on This Topic
View all →Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Explaining Explanations: An Overview of Interpretability of Machine Learning
RISE: Randomized Input Sampling for Explanation of Black-box Models
Mitigation Tools
Tools related to this risk domain, via shared responsible AI topics.
Open Source
View all →AIF360
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
⭐ 2,872
fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
⭐ 2,199
responsible-ai-toolbox
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.