Human-Computer Interaction and Collective Intelligence

The lessons of HCI can therefore be brought to bear on different aspects of collective intelligence. On the one hand, the people in the collective (the crowd) will only contribute if there are proper incentives and if the interface guides them in usable and meaningful ways. On the other, those interested in leveraging the collective need usable ways of coordinating, making sense of, and extracting value from the collective work that is being done, often on their behalf. Ultimately, collective intelligence involves the co-design of technical infrastructure and human-human interaction: a socio-technical system. In crowdsourcing, we might differentiate between two broad classes of users: requesters and crowd members. The requesters are the individuals or group for whom work is done or who takes the responsibility to aggregate the work done by the collective. The crowd member (or crowd worker) is one of many people to contribute. While we often use the word “worker,” crowd workers do not have need to be (and often aren’t) contributing as part of what we might consider standard “work.” They may work for pay or not, work for small periods of time or contribute for days to a project they care about, and they may work in such a way as each individual’s contribution may be difficult to discern from the collective final output. HCI has a long history of studying not only the interaction between individuals with technology, but also the interaction of groups with or mediated by technology. For example, computer-supported cooperative work (CSCW) investigates how to allow groups to accomplish tasks together using a shared or distributed computer interfaces, either at the same time or asynchronously. Current crowdsourcing research alters some of the standard assumptions about the size, composition, and stability of these groups, but the fundamental approaches remain the same. For instance, workers drawn from the crowd may be less reliable than groups of employees working on a shared task, and group membership in the crowd may change more quickly. There are three main vectors of study for HCI and collective intelligence. The first is directed crowdsourcing, where a single individual attempts to recruit and guide a large set of people to help accomplish a goal. The second is collaborative crowdsourcing, where a group gathers based on shared interest and self-determine their organization and work. The third vector is passive crowdsourcing, where the crowd or collective may never meet or coordinate, but it is still possible to mine their collective behavior patterns for information. We cover each vector in turn. We conclude with a list of challenges for researches in HCI related to crowdsourcing and collective intelligence.