Summary：
The author in this paper indicated that interactive machine learning can promote the democratization of applied machine learning, which enables users to make use of machine learning-based systems to satisfy their own requirements. However, achieving effective end-user interaction through interactive machine learning brings new challenges. To addressing these challenges and highlight the role and importance of users in the interactive machine learning process, the author presented case studies and the discussion based on the results. For the first section of the case studies presented in the paper indicate that end-user always expect richer involvement in the interactive machine-learning process than just label instances or as an oracle. Besides, the transparency of system work could improve the user experience and the accuracy of the resulting models. Then, the case studies in the next sections indicate richer user interactions were beneficial within a limited boundary, and may not be appropriate for all scenarios. Finally, the author discussed the challenges and opportunities for interactive machine learning systems such as the desire for developing common language across diverse fields, etc.
Reflection:
Personally, I am not very familiar with machine learning. However, after reading this paper, I think the interactive machine learning system could amplify the effects of machine learning on our daily life to a great extent. Especially users with no or little machine learning knowledge could involve in the learning process could not only improve the accuracy of learning outcomes but also richer the interaction between users and products.
One typical example I have experienced the interactive machine learning is one of the features of Netease Cloud Music Player – Private Radio. The private radio recommends music you may like based on your playlist, and then require your feedback, which is like or not. The more feedback you provided, the more likely you would like the next recommendation. Thus, the user study results presented in the paper that end-user would like richer interactive is reasonable. I would also like to tag the recommend music not just like or not, which may also include the reason such as I like this because of the melody or lyrics.
I also agree with the scenario that transparency can help people provide better labels. In my opinion, the transparency of how system works have the same effect as providing users feedback on how their operations influenced the system. A good understanding of the impact of users’ actions would allow them to proactively five more accurate feedback. Regard as the Music Player example, if my private radio always recommends music I like, in order to hear more good music, I will more willing to provide feedback. Conversely, if my feedback has no influence on the radio recommendation situation, I will just give up this feature.
Questions:
- Do you have a similar experience in the interactive machine-learning system?
- What is your expectation of these systems?
- What do you think of the tradeoff between machine learning and human-computer interaction in this interactive learning system?
- Talk about any of the challenges faced by the interactive learning system which demonstrated at the end of the paper.
When I did online shopping using Amazon or Taobao, the recommendation system uses an interactive machine-learning system. It is for sure that I want the system to always recommend me stuff which I need. Though this technology brings us convenience, I think there are still ethical issues in it. For the users, it is hard to know whether all the collected user activities data are used for the system which they are using. Instead, some of the companies may use that data for researches which are not authenticated by the user.