  This papers studies how competition affects machine learning (ML) predictors.
As ML becomes more ubiquitous, it is often deployed by companies to compete
over customers. For example, digital platforms like Yelp use ML to predict user
preference and make recommendations. A service that is more often queried by
users, perhaps because it more accurately anticipates user preferences, is also
more likely to obtain additional user data (e.g. in the form of a Yelp review).
Thus, competing predictors cause feedback loops whereby a predictor's
performance impacts what training data it receives and biases its predictions
over time. We introduce a flexible model of competing ML predictors that
enables both rapid experimentation and theoretical tractability. We show with
empirical and mathematical analysis that competition causes predictors to
specialize for specific sub-populations at the cost of worse performance over
the general population. We further analyze the impact of predictor
specialization on the overall prediction quality experienced by users. We show
that having too few or too many competing predictors in a market can hurt the
overall prediction quality. Our theory is complemented by experiments on
several real datasets using popular learning algorithms, such as neural
networks and nearest neighbor methods.
