Algorithmic disgorgement
An enforcement remedy, most notably used by the US Federal Trade Commission, requiring destruction of a model or algorithm derived from unlawfully collected data, treating the model as a fruit of the unlawful collection in the same way courts treat ill-gotten financial gains as subject to disgorgement.
Defined in 3 GAGE programs, which carry 9 distinct definitions of it. The wording above is taught in AI Data Governance: The Data Chair.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
An enforcement remedy, most notably used by the US Federal Trade Commission, requiring destruction of a model or algorithm derived from unlawfully collected data, treating the model as a fruit of the unlawful collection in the same way courts treat ill-gotten financial gains as subject to disgorgement.
An enforcement remedy (used by the United States Federal Trade Commission) ordering deletion of a model and the data it was trained on. Named here to contrast the mandatory pile (a real legal consequence) with the voluntary pile (a certificate, which cannot prevent such a remedy). (see Topic 6.1)
A remedy, used by the US FTC, requiring a company to delete models and algorithms trained on improperly obtained data. Severe because it can destroy the core asset a business was built on.
A regulatory remedy, most notably used by the US FTC, requiring destruction not only of improperly obtained data but of any algorithm or model trained on it.
A regulatory remedy, most clearly demonstrated by the US Federal Trade Commission's 2021 order against Everalbum, Inc., requiring an organization to delete or destroy not only unlawfully collected data but also the models or algorithms built using that data.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Purpose archaeology: what your data was collected FOR versus what it feeds now · Consent, Purpose, and the Law of Data, AI Data Governance: The Data Chair
- Retention versus the model that memorized: deleting data a model already learned from · Consent, Purpose, and the Law of Data, AI Data Governance: The Data Chair
- The deletion decision: the terabytes your organization should destroy this quarter, defended · The Money of Data, AI Data Governance: The Data Chair
- The wrong data fed the model: the recall decision for an AI system in production · Data Incidents, AI Data Governance: The Data Chair
- The deletion that was not: personal data found in a model trained last year · Data Incidents, AI Data Governance: The Data Chair
- Machine unlearning: the emerging answer to deleting what a model learned · The Frontier Discipline, AI Data Governance: The Data Chair
- The US mosaic: federal signals, state laws, and the agencies that already reach workplace AI · The World's Rulebooks, AI Governance: Applied Mastery
- Standards versus law: what certification buys you and what it never will · The World's Rulebooks, AI Governance: Applied Mastery
- Regulatory Compliance Navigator · Governance and Responsible AI, Business AI Transformation
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.