Hi all. This is my last blog post for this project. The last few weeks, we spent little time working on the project and instead focused on finishing up our classes. To be honest, we lost some motivation after finishing the paper, and we've been reluctant to start up new tasks so close to the end of the year. In this post, I'd like to talk about some of the things I've learned in this project other than what we found in our research. This post is really more a service to myself; to help me reflect on this past year and what I've learned. When I started this project, I didn't understand how research in computing worked. My exposure to research had been more biology/clinical, in which research involved setting a hypothesis about some protein, designing an experiment with positive and negative controls, and then running the experiment repeatedly until confident in the results. In computing research, my experience has been of two varieties: reading papers and proce...
This week, Diana and I looked for some datasets on which to evaluate our fairness criteria. This is no simple task. For one, the datasets need to be large enough to be split into training, calibration, and testing sets. Second, there needs to be some protected group attribute like sex, race, or age. Third, there needs to be some outcome ranking as well as some truth value. The truth is often the hardest attribute to find. In this post, I introduce some of the datsets we decided to use. I try to identify each of the necessary features that we need to properly test our fairness criteria. COMPAS The COMPAS dataset is an obvious choice because it is well ingrained in the literature about fairness. It has 18,000 observations (n=18000) and 1000 attributes (k=1000). Protected groups can be sex (81/19) or race (51/49). The outcome attribute can be the recidivism score, the score used in court that represents the risk that the accused will commit another crime. The truth attribute is a li...
Last week, I wrote about our new plan to write a visualization paper based on the IQP team's ranking application. At conferences like VAST, there are several different types of papers one can submit, and our first challenge was deciding where to pigeonhole our application. After talking with Professor Harrison, a notable vis expert in our CS department, we determined that the novelty in our project is the user interface. As such, our paper would fall into the category of systems or design study. According to Processes and Pitfalls in Writing Information Visualization Research Papers , a sort-of guidebook to the world of writing vis, systems papers focus on architecture choices and the design of abstractions. Design study papers, in contrast, demonstrate a new visual design to solve a problem, often using existing algorithms or techniques. If we consider ours to be a systems paper, we would probably focus on explaining the pipeline (from choosing a dataset to building a ranking from...
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