Exam AIGP topic 1 question 68 discussion

The technique to remove the effects of improperly used data from a machine learning system is: A. Data cleansing. Data cleansing (or data cleaning) is the process of identifying, correcting, or removing corrupt, inaccurate, irrelevant, or improperly used data from a dataset before, during, or after training and deployment. This ensures higher data integrity, accuracy, and reliability of the model's predictions and decisions by systematically detecting and eliminating the impact of erroneous inputs.

Model disgorgement is the technique used to remove the effects of improperly used data from a machine learning system. It involves: 🔁 Retraining or replacing a model to eliminate any influence from data that was obtained, used, or processed in violation of legal, ethical, or policy standards (e.g., without consent, violating terms of service, or using biased or discriminatory data).