  Early supervised machine learning algorithms have relied on reliable expert
labels to build predictive models. However, the gates of data generation have
recently been opened to a wider base of users who started participating
increasingly with casual labeling, rating, annotating, etc. The increased
online presence and participation of humans has led not only to a
democratization of unchecked inputs to algorithms, but also to a wide
democratization of the "consumption" of machine learning algorithms' outputs by
general users. Hence, these algorithms, many of which are becoming essential
building blocks of recommender systems and other information filters, started
interacting with users at unprecedented rates. The result is machine learning
algorithms that consume more and more data that is unchecked, or at the very
least, not fitting conventional assumptions made by various machine learning
algorithms. These include biased samples, biased labels, diverging training and
testing sets, and cyclical interaction between algorithms, humans, information
consumed by humans, and data consumed by algorithms. Yet, the continuous
interaction between humans and algorithms is rarely taken into account in
machine learning algorithm design and analysis. In this paper, we present a
preliminary theoretical model and analysis of the mutual interaction between
humans and algorithms, based on an iterated learning framework that is inspired
from the study of human language evolution. We also define the concepts of
human and algorithm blind spots and outline machine learning approaches to mend
iterated bias through two novel notions: antidotes and reactive learning.
