Data in digital form is expanding at an exponential rate, far outpacing any chance of getting any significant fraction labelled manually. This has resulted in heightened research emphasis on unsupervised learning, learning in the absence of labels. In fact, unsupervised learning has been often dubbed as the next frontier of AI. Unsupervised learning is the most plausible model to analyze the bulk of passively collected data that spans across various domains; e.g., social media footprints, safety/surveilance cameras, IoT devices, sensors, smartphone apps, medical wearables, traffic sensing devices and public wi-fi access. While fairness in supervised learning, such as classification tasks, has inspired a large amount of research in the past few years, work on fair unsupervised learning has been relatively slow in picking up. This tutorial targets to provide an overview of: (i) fairness issues in unsupervised learning drawing abundantly from political philosophy, (ii) current research in fair unsupervised learning, and (iii) new directions to extend the state-of-the-art in fair unsupervised learning. While we intend to broadly cover all tasks in unsupervised learning, our focus will be on clustering, retrieval and representation learning. In a unique departure from conventional data science tutorials, we will place significant emphasis on presenting and debating pertinent literature from ethics and philosophy. Overall, this half-day tutorial brings a strong emphasis on ensuring strong interdisciplinarity.