In this paper, the authors conducted literature review on papers and publications which represent the state of the art in human-computer collaboration and human computation area. From these literature review, they identified the affordance into two groups: human-intelligence and machine-intelligence. Though they introduced the affordance can be split into two groups, there is also systems like reCAPTCHA and PatViz which can benefit from the combination of the two intelligences. Finally, they provided examples of how to utilize this framework and some advice on future researches. They announced that human adaptability and machine sensing is two extension of current work. Also, future work (find way to measure human work, assess human work in practice and account for individual differences in human operators) need combination of experts in theoretical computer science, as well as in psychology and neuroscience.
Reflections:
Primarily, I felt that both human affordance and machine affordance contribute to the success of current systems. There is great importance in allocate the tasks and make human and machine can support each other. Current systems may suffer from poor human-computer collaboration. For example, systems cannot assign proper work to human workers or the user interface is difficult to use. To avoid such kind of situation, policies and guidance are needed. There should be a common used evaluation criteria and make restriction on industry.
Secondly, researchers can benefit from making an overview of the related research areas. In most of the cases, to solve a problem we may need help from experts in different areas. As a result, the category of the problem may become ambiguous. As a result, researchers from different aspects may waste their effort on similar researches and they may be not able to get help from previous research in another area. For this reason, it is important to make a category of the researches with similar goals or related techniques. Current or future researches will benefit from such kind of category and the discussions between experts will become much easier. As a result, more ideas can be proposed and researchers can find out some fields which they have not been considered before.
Additionally, in the human-computation and human-computer collaborative systems, problems are solved using both human-intelligence and machine-intelligence. For such a comprehensive area, it is important to do reflection regularly. With the reflections, researchers can make comprehensive consideration on the problems they are going to solve. With the table in the paper, the affordance of human-intelligence and machine-intelligence can be overviewed. Additionally, we can find out in which areas there have been a lot of research and to which areas we should pay more attention to. With this common framework, the understanding and discussion on previous work would be much easier and novel ideas will occur. This kind of reflection can be applied in other areas too, which will result in rapid development in each industry.
Questions:
Why there is no updates in systems which is considered hard to use?
How to assess human work and machine work in practical?
For user interface, is it more important to let new workers use easily but there is limitation in customization or let experienced workers able to customize and reach high efficiency however new user may face some difficulty in using?
First of all, I agree with what you said about regularly reflecting on the past results, we can constantly summarizing and distinguishing different areas in that process, so that new issues can be studied more effectively in the future. For a user interface, I think it is more important for experienced employees to achieve better efficiency, because people will have a little difficulty when they are exposed to new things. If you sacrificed functions for the experience of new workers, I think it is not worthy.