Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about the data type or other domain knowledge. The generality and domain agnosticism in combination with their simplicity make autoencoders a perfect sandbox for researching and developing novel (deep) clustering algorithms. Clustering methods group data based on similarity, a task that benefits from the lower-dimensional representation learned by an autoencoder, mitigating the curse of dimensionality. Specifically, the combination of deep learning with clustering, called Deep Clustering, enables to learn a representation tailored to specific clustering tasks, leading to high-quality results. This survey provides an introduction to fundamental autoencoder-based deep clustering algorithms that serve as building blocks for many modern approaches.

Artificial Intelligence (AI) algorithms are widely employed by businesses, governments, and other organizations in order to make decisions that have far-reaching impacts on individuals and society. Their decisions might influence everyone, everywhere, and anytime, offering solutions to problems faced in different disciplines or in daily life, but at the same time entailing risks like being denied a job or a medical treatment. The discriminative impact of AI-based decision-making to certain population groups has been already observed in a variety of cases. For instance, the COMPAS system for predicting the risk of re-offending was found to predict higher risk values for black defendants (and lower for white ones) than their actual risk (Angwin, Larson, Mattu, & Kirchner, 2016) (racial-bias). In another case, Google's Ads tool for targeted advertising was found to serve significantly fewer ads for high paid jobs to women than to men (Datta, Tschantz, & Datta, 2015) (gender-bias).