Generative ML and CSAM: Implications and Mitigations
Abstract/Contents
- Abstract
- A joint report between the Stanford Internet Observatory and Thorn examines implications of fully realistic child sexual abuse material (CSAM) produced by generative machine learning models. Advances in the open-source generative ML community have led to increasingly realistic adult content, to the point that content indistinguishable from actual photographs is likely to be common in the very near future. These same models and techniques have been also leveraged to produce CSAM. We examine what has enabled this state of affairs, the potential societal consequences of the proliferation of such content, and measures that can be taken to minimize harm from current and future visual generative ML models.
Description
Type of resource | text |
---|---|
Date created | June 24, 2023 |
Date modified | June 24, 2023; June 24, 2023 |
Publication date | June 24, 2023 |
Creators/Contributors
Author | Thiel, David | https://orcid.org/0000-0002-0947-5921 (unverified) |
---|---|---|
Author | Stroebel, Melissa | https://orcid.org/0000-0003-2674-4465 (unverified) |
Author | Portnoff, Rebecca | |
Research team head | Hancock, Jeffrey | https://orcid.org/0000-0001-5367-2677 (unverified) |
Researcher | Scyphers, Cassandra | |
Researcher | O'Gorman, Tim | https://orcid.org/0000-0001-6951-2117 (unverified) |
Contributor | DiResta, Renée | https://orcid.org/0000-0001-9229-9713 (unverified) |
Subjects
Subject | Generative Machine Learning |
---|---|
Subject | AI Ethics |
Subject | CSAM |
Genre | Text |
Genre | Report |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International license (CC BY-NC-ND).
Preferred citation
- Preferred citation
- Thiel, D., Stroebel, M., and Portnoff, R. (2023). Generative ML and CSAM: Implications and Mitigations. Stanford Digital Repository. Available at https://purl.stanford.edu/jv206yg3793. https://doi.org/10.25740/jv206yg3793.
Collection
Stanford Internet Observatory, Freeman Spogli Institute for International Studies
View other items in this collection in SearchWorksContact information
- Contact
- internetobservatory@stanford.edu
Also listed in
Loading usage metrics...