Many researchers’ primary goals are to develop tools and methodologies that can facilitate human-machine collaborative problem solving and to understand and maximize the benefits of the partnership of size and complexity. The first problem is how do we tell if a problem would benefit from a collaborative technique? This paper mentioned that even though deploying various collaborative systems has led to many novel approaches to difficult problems, it has also led to the investment of significant time, expense and energy. However, these problems might be solved better by only depending on human or machine techniques. The second problem is how do we decide which tasks to delegate to which party and when? The authors stated that we are still lacking a language for describing the skills and capacity of the collaborating team. For the third question, how does one system compare to others trying to solve the same problem? Lacking of no common language or measures by which to describe new systems is one important reason. About the research contributions, authors picked out 49 publications from 1271 papers which represent the state of the art in the study of human-computer collaboration and human computation. Then authors identify grouping based on human- and machine-intelligence affordances which form the basis of a common framework for understanding and discussing collaboration works. Last, the authors talked about the unexplored areas for future work. Each of the current frameworks is specific to a subclass of collaborative systems which is hard to extend them to a broader class of human-computer collaborative systems.
Based on the definition of “affordance”, I know both humans and machines bring to the partnership opportunities for action, and each mush be able to perceive and access these opportunities in order for them to be effectively leveraged. It is not surprised for me that the bandwidth of information presentation is potentially higher in the visual perception than any of the other senses. I consider that visual perception as the most important information processing for humans in most cases, that’s why there are a plethora of research studies combined with human visual processing to solve various problems. I am quite interested in the concept of sociocultural awareness. Individuals understand their actions in relation to others and to the social, cultural and historical context in which they are carried out. I think this is a paramount view in the study of HCI. Different individuals in different environments with different cultural backgrounds would behave different interactions with the same computers. In the future, I consider that cultural background should become an important factor in the studies of HCI.
I found that various applications are categorized into multiple affordances. If so, how can the authors answer the third question? For example, if two systems are trying to solve the same problem, but each of them have different human or computer affordance, how can I say which is better? Does different affordance have different weight values? Or we should treat them equally?
Less tools are designed for creativity, social and bias-free affordance, what does this mean? Is it mean that these affordances are less important or researchers are still working on these areas?
