Human-machine-learning integration and task allocation in citizen science

The field of citizen science involves the participation of citizens across different stages of a scientific project; within this field there is currently a rapid expansion of the integration of humans and AI computational technologies based on machine learning and/or neural networking-based paradigms. The distribution of tasks between citizens ("the crowd"), experts, and these technologies has received relatively little attention. To illustrate the current state of task allocation in citizen science projects that integrate humans and computational technologies, an integrative literature review of 50 peer-reviewed papers was conducted. A framework was used for characterizing citizen science projects based on two main dimensions: (a) the nature of the task outsourced to the crowd, and (b) the skills required by the crowd to perform a task; the framework was extended to include tasks performed by experts and AI computational technologies as well. Most tasks citizens perform in the reported projects are well-structured, involve little interdependence, and require skills prevalent among the general population. Experts’ work is typically structured and at a higher level of interdependence, requiring expertise in specific fields. Unsurprisingly, AI computational technologies are capable of performing mostly well-structured tasks at a high level of interdependence. The paper argues that the resulting task distribution from the combination of computation and citizen science may disincentivize certain volunteer groups. Assigning tasks in a meaningful way to citizen scientists alongside experts and AI computational technologies is an unavoidable design challenge.

Marisa Ponti, Alena Seredko