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Read: Technical methods of execution used by researchers and engineers and how to approach algorithmic disgorgement through various operational strategies outlined in ¹PNAS AI Model Disgorgement Study.
1. Machine Unlearning (Reactive): Using mathematical techniques and noise adjustment (influence functions) to make a trained model "forget" specific data points, though it balances heavy computing costs against model accuracy.
2. Data Compartmentalization (Proactive): Splitting training sets into isolated shards so that specific sub-models can be dropped or detached if a compliance issue arises.
3. Differential Privacy (Preemptive): Injecting mathematical noise during the initial training phase to limit how much any single data point influences the final model.
¹A. Achille, M. Kearns, C. Klingenberg, and S. Soatto, “AI model disgorgement: Methods and choices | PNAS,” PNAS, https://t.co/lA0WVzadHB (accessed Nov. 13, 2026).
https://t.co/SejdG2XxKC Edited by Jeffrey Ullman, Stanford University (Retired), Stanford, CA; received July 12, 2023; accepted March 15, 2024.
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