  Machine learning is pervasive. It powers recommender systems such as Spotify,
Instagram and YouTube, and health-care systems via models that predict sleep
patterns, or the risk of disease. Individuals contribute data to these models
and benefit from them. Are these contributions (outflows of influence) and
benefits (inflows of influence) reciprocal? We propose measures of outflows,
inflows and reciprocity building on previously proposed measures of training
data influence. Our initial theoretical and empirical results indicate that
under certain distributional assumptions, some classes of models are
approximately reciprocal. We conclude with several open directions.
