Category Archives: Adversarial Machine Learning
Training Machine Learning to Anticipate Manipulation
The paper “Training Machine Learning to Anticipate Manipulation” (Blumenstock et al., 2023) addresses a fundamental flaw in standard machine learning: the assumption that data distributions remain stable when models are deployed. In reality, when algorithms make consequential decisions—such as awarding loans or granting parole—individuals have strong incentives to “game” the system by strategically altering their […]