I was going through the paper "The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction" by google on which suggests best practices for models in production. In a section about privacy controls in the data pipeline it says:
Finally, test that any user-requested data deletion propagates to the data in the ML training pipeline, and to any learned models.
I understand about the data deletion from the data pipeline but is it even possible to "un-learn" a single training example without retraining on the new data? They have mentioned in the paper that the practices are being used in google at some point or other, so there might be an efficient way but I'm unable to get any information on this.
I am looking for any literature on this or any ideas about how one would go on solving this problem.
Edit: On further research, I found this paper which focuses on the specific problem. Though making a lot of assumptions they propose a method for k-means too. Looks like this is an upcoming research area and would require time to develop!