Predictive models often include group attributes that encode personal characteristics like sex, blood type, or HIV status. Personalized models must ensure fair use – i.e., groups who provide personal data should expect to receive a tailored improvement in accuracy compared to a non-personalized model. In this paper, we derive conditions under which one can detect fair use violations in predictive models and characterize when estimating fair use is impossible. We propose a metric to evaluate the…