On the Rival Nature of Data: Tech and Policy Implications
Ayelet Gordon-Tapiero, Katrina Ligett, Kobbi Nissim · 2025
Data is often thought of and treated as a non-rival good, which would imply that one person's use of data does not inherently diminish its availability for others. Building on research in privacy and statistics, we argue that there exist many important settings in which data should be treated as a rival good. Our argument takes into account modern uses of data for statistics, machine learning, and a variety of other purposes, in conjunction with requirements of privacy protection and statistical validity. Excessive sharing or reuse of data about individuals can lead to leakage of sensitive personal information, potentially causing harm to those whose information is included in the data. Overuse of data in statistics or machine learning can lead to overfitting, i.e., models that perform well on training data but poorly on fresh unseen data.