Background

In the 1960s, Engelbart and Licklider (both managed research programs at DARPA) pioneered the arguments for “human-computer symbiosis”(Licklider 1960). A fundamental assumption behind the need for human-computer symbiosis is that computers and human brains have different problem-solving capabilities. As such, IA research pursues design ideas that are intended to optimize the combined computational potential of human beings and computers. One branch of IA very familiar to information systems researchers is Human Computer Interaction (HCI). One of the pioneers of the HCI approach, Terry Winograd, has commented on the tensions between the AI and the HCI camps, and the associated “rationalistic” and “design” perspectives that they represent (Winograd 2006). Some parts of AI attempted to model human beings as cognitive machines and sought to build human-like AI systems. HCI, on the other hand, focused on a design approach which emphasizes interpretation, human behavior, and experimentation. Winograd quotes David Kelley, the renowned design thinker, as saying:“Enlightened trial and error outperforms the planning of flawless intellect,” suggesting the importance of iteratively improving by modeling the interaction between humans and AI. However, HCI is not the only perspective to human-computer symbiosis. Large scale computational problems often cannot be solved by either computer or humans alone—such problems are termed “human computation problems”(Von Ahn and Dabbish 2008). For instance, crowdsourcing strategies for many messy large-scale image or character recognition problems fall into this domain. Human computation problems rely on harnessing human processing power (ie, common sense) to solve problems that computers are not yet good at solving. More interestingly, many early human computation problem-solving approaches have utilized gamification strategies that seem to be very well aligned with the HCI tradition of “design approach.”