With the Rankit paper submitted, it was time for me to change gears and dive into the research with MaryAnn.
For me, this week revolved around getting up to speed with the fair ranking research. I read over the current in progress fair ranking paper and attended meetings where MaryAnn and Caitlin helped familiarize me with the code base and research that they've completed.
Taking into account the feedback received from the our mentors, we updated the section analyzing the outcome of the online user study. We updated the machine learning section to include more references and added more charts to the whole paper.
The team also discussed the next steps and observed new features to be implemented.
In order to meet the deadline, the development team has been on a strict schedule to complete all tasks necessary.
Because we will be applying to a visualization conference, Rankit needs to have more visualization features. Therefore, we've been working on integrating active learning into the tool. With active learning, our tool will have immediate and engaging feedback on the ranking as the user decides whether they should rank more items to get better results or if they are satisfied with the ranking as is and can stop.
The features I worked on were to make the Explore tool more robust. One feature is to work on highlighting the rows of the data table with a gradient signifying confidence.
The second feature was to have a bar for each row signifying the score of the object.