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Early supervised machine learning algorithms have relied on reliable expertlabels to build predictive models. However, the gates of data generation haverecently been opened to a wider base of users who started participatingincreasingly with casual labeling, rating, annotating, etc. The increasedonline presence and participation of humans has led not only to ademocratization of unchecked inputs to algorithms, but also to a widedemocratization of the "consumption" of machine learning algorithms' outputs bygeneral users. Hence, these algorithms, many of which are becoming essentialbuilding blocks of recommender systems and other information filters, startedinteracting with users at unprecedented rates. The result is machine learningalgorithms that consume more and more data that is unchecked, or at the veryleast, not fitting conventional assumptions made by various machine learningalgorithms. These include biased samples, biased labels, diverging training andtesting sets, and cyclical interaction between algorithms, humans, informationconsumed by humans, and data consumed by algorithms. Yet, the continuousinteraction between humans and algorithms is rarely taken into account inmachine learning algorithm design and analysis. In this paper, we present apreliminary theoretical model and analysis of the mutual interaction betweenhumans and algorithms, based on an iterated learning framework that is inspiredfrom the study of human language evolution. We also define the concepts ofhuman and algorithm blind spots and outline machine learning approaches to menditerated bias through two novel notions: antidotes and reactive learning.

Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework