Abstract

Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, people and algorithms are increasingly engaged in interactive processes wherein neither the human nor the algorithms receive unbiased data. Algorithms can also make biased predictions, leading to what is now known as algorithmic bias. On the other hand, human’s reaction to the output of machine learning methods with algorithmic bias worsen the situations by making decision based on biased information, which will probably be consumed by algorithms later. Some recent research has focused on the ethical and moral implication of machine learning algorithmic bias on society. However, most research has so far treated algorithmic bias as a static factor, which fails to capture the dynamic and iterative properties of bias. We argue that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms’ performance. For this purpose, we present an iterated-learning framework that is inspired from human language evolution to study the interaction between machine learning algorithms and humans. Our goal is to study two sources of bias that interact: the process by which people select information to label (human action); and the process by which an algorithm selects the subset of information to present to people (iterated algorithmic bias mode). We investigate three forms of iterated algorithmic bias (personalization filter, active learning, and random) and how they affect the performance of machine learning algorithms by formulating research questions about the impact of each type of bias. Based on statistical analyses of the results of several controlled experiments, we found that the three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms. We also found that iterated filter bias, which is prominent in personalized user interfaces, can lead to more inequality in estimated relevance and to a limited human ability to discover relevant data. Our findings indicate that the relevance blind spot (items from the testing set whose predicted relevance probability is less than 0.5 and who thus risk being hidden from humans) amounted to 4% of all relevant items when using a content-based filter that predicts relevant items. A similar simulation using a real-life rating data set found that the same filter resulted in a blind spot size of 75% of the relevant testing set.

Highlights

  • Websites and online services offer large amounts of information, products, and choices

  • We investigated three forms of iterated algorithmic bias and how they affect the performance of machine learning algorithms by formulating research questions about the impact of each type of bias

  • Based on statistical analysis of the results of several controlled experiments using synthetic and real data, we found that: 1. The three different forms of iterated algorithmic bias, do affect algorithm performance when fixing the human interaction probability to 1

Read more

Summary

Introduction

Websites and online services offer large amounts of information, products, and choices This information is only useful to the extent that people can find what they are interested in. All existing approaches aid people by suppressing information that is determined to be disliked or not relevant. Collaborative Filtering (CF) [10, 17, 25,26,27,28,29,30], on the other hand, does not require item attributes or user attributes Rather it makes predictions about what a user would like based on what other similar users liked. E.g. K-nearest neighbors [31, 32] and non-negative matrix factorization (NMF) [33,34,35,36,37], that have close analogs in the psychology literatures on concept learning, e.g. exemplar models [38,39,40] and probabilistic topic models [41, 42]

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call