BHCC 2020

Human Computation and Crowdsourcing have become ubiquitous in the world of algorithm augmentation and data management. However, humans have various cognitive biases that influence the way they make decisions, remember information, and interact with machines. It is thus important to identify human biases and analyse their effect on complex hybrid systems. On the other hand, the potential interaction with a large pool of human contributors gives the opportunity to detect and handle biases in existing data and systems.

The goal of this symposium is to analyse both existing human biases in hybrid systems, and methods to manage bias via crowdsourcing and human computation. We will discuss different types of biases, measures and methods to track bias, as well as methodologies to prevent and solve bias.

An interdisciplinary approach is often required to capture the broad effects that these processes have on systems and people, and at the same time to improve model interpretability and systems’ fairness.

We will provide a framework for discussion among scholars, practitioners and other interested parties, including industry, crowd workers, requesters and crowdsourcing platform managers. We expect contributions combining ideas from different disciplines, including computer science, psychology, economics and social sciences.

wordcloud obtained from previous year authors abstracts