LAION-400M image-text pairing dataset

LAION-400M is a large, open dataset of 400 million image and text pairings. 

Developed by German non-profit collective LAION and launched in 2020, LAION-400M was used to train Imagen, Lensa, Stable Diffusion, and other text-to-image models.

The dataset's successor LAION-5B comprises 5 billion pairings.

Risks and harms 🛑

The LAION-400M dataset is accused of violating privacy, enabling the generation of offensive, hateful, explicit, and derogatory content, and perpetuating biases due to unfiltered, large-scale web-scraped data.

Transparency and accountability 🙈

The LAION-400M dataset is seen to have several important transparency limitations.

• Source data. While LAION-400M contains image-text pairs scraped from the internet, there is limited transparency about the specific websites and sources used.

• Scraping methodology. Details about the exact web scraping techniques and filtering processes are not fully disclosed.

• Consent and copyright. It's unclear if proper consent was obtained for all images and text, or how copyright issues were addressed.

• Demographic representation. There's limited information on the demographic distribution and potential biases in the dataset.

• Content moderation. The extent and methods of content moderation to remove inappropriate or offensive material are not fully detailed.

• Quality assessment. Criteria for assessing the quality and relevance of image-text pairs are not thoroughly explained.

• Versioning and updates. Information about dataset versions, updates, or changes over time may be limited.

• Downstream impact. There's a lack of comprehensive analysis on how using this dataset impacts AI models trained on it.

Research, advocacy 🧮

• Birhane A., Luccioni A.S. et al (2023). Into the LAIONs Den: Investigating Hate in Multimodal Datasets

• Thiel D., Hancock J. (2023). Identifying and Eliminating CSAM in Generative ML Training Data and Models

• Birhane A., Prabhu V.U., Kahembwe E. (2021). Multimodal datasets: misogyny, pornography, and malignant stereotypes