Crowd Science Workshop — Remoteness, Fairness, and Mechanisms as Challenges of Data Supply by Humans for Automation

Despite the obvious advantages, automation driven by machine learning and artificial intelligence carries pitfalls for the lives of millions of people. The pitfalls include disappearance of many well-established mass professions and increasing consumption of labeled data produced by humans. Those data suppliers are often managed by old fashioned approach and have to work full-time on routine pre-assigned task types. Crowdsourcing methodology can be considered as a modern and effective way to overcome these issues since it provides flexibility and freedom for task executors in terms of place, time and the task type they want to work on. However, many potential stakeholders of crowdsourcing processes hesitate to use this technology due to a series of doubts (that have not been removed during the past decade). In order to overcome this, we organize this workshop which will focus research and industry communities on three important aspects: Remoteness, Fairness, and Mechanisms.

Topics: Crowdsourcing, Data Annotation, Data Labeling, Human Computation, Remote Work, Remoteness, Fairness, Mechanisms