Neglected Risks: The Disturbing Reality of Children's Images in Datasets and the Urgent Call for Accountability
This addresses privacy and exploitation risks for children in AI research, though it is incremental as it builds on existing models and calls for community action.
The paper tackles the ethical problem of children's images in AI datasets, proposing a pipeline to detect and remove such images, which was tested on datasets like #PraCegoVer and Open Images V7, achieving baseline effectiveness.
Including children's images in datasets has raised ethical concerns, particularly regarding privacy, consent, data protection, and accountability. These datasets, often built by scraping publicly available images from the Internet, can expose children to risks such as exploitation, profiling, and tracking. Despite the growing recognition of these issues, approaches for addressing them remain limited. We explore the ethical implications of using children's images in AI datasets and propose a pipeline to detect and remove such images. As a use case, we built the pipeline on a Vision-Language Model under the Visual Question Answering task and tested it on the #PraCegoVer dataset. We also evaluate the pipeline on a subset of 100,000 images from the Open Images V7 dataset to assess its effectiveness in detecting and removing images of children. The pipeline serves as a baseline for future research, providing a starting point for more comprehensive tools and methodologies. While we leverage existing models trained on potentially problematic data, our goal is to expose and address this issue. We do not advocate for training or deploying such models, but instead call for urgent community reflection and action to protect children's rights. Ultimately, we aim to encourage the research community to exercise - more than an additional - care in creating new datasets and to inspire the development of tools to protect the fundamental rights of vulnerable groups, particularly children.