Unintended Interactions among ML Risks and Defenses


Machine learning models are susceptible to a wide range of risks to security, privacy, and fairness. Several defenses have been proposed to mitigate these risks. However, defending against a specific risk can result in an unintended increase (or decrease) in susceptibility to other risks. Similarly when defenses against multiple risks are applied to a machine learning model, there could be conflicting interactions among among them. This blog article provides additional context for this work.

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