Week 32-33: Wrapping up
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 processing my thoughts about them, and building a program and then talking about it. Certainly, both of these have required some amount of hypothesis testing, but the goal seems more to be of convincing others that my thoughts on a problem are valid and less on experiment-driven data. Since we did not complete our work on fairness in ranking, I would imagine this experiment side also exists but that we didn't quite get to it.
Reading back on my posts, it's frustrating to see how long it took to make progress. With regards to fairness, we had a basic idea of what we wanted to do, but the methodology didn't exist for the ranking context. I feel pretty happy now with how we've framed the problem, but we spent a long time thinking about fairness in classification and how to transform it to ranking.
I think one big difference in this project and my schoolwork is the lack of well-defined tasks. In my classes, I am used to being given an assignment where, even if the assignment is purposefully vague, the professors and teaching assistants have a good idea what needs to be done and unconsciously give hints as to good work strategies. In this project, some objectives took much longer to complete while others were relatively easy. For example, it was incredibly difficult to find datasets on which to evaluate fairness because we needed some truth variable. On the other hand, implementing isotonic regression to correct for fairness was a one-liner in Python. Much time was spent thinking about what to do next, but looking back makes all the tasks look deceptively straight-forward.
Writing is hard. I have grown to appreciate these blogs because they force me to write about my thoughts. While I don't believe they're very well written, it has at the very least been a good exercise for me to reflect on the past week. I plan to continue writing a blog with future research projects. Writing the paper was even more hard because I knew someone would eventually read it. It was challenging for me to explain my thoughts in coherent sentences, and I kept falling into a pattern of repeating myself or using the same words. However, it was very satisfying when I occasionally did write a sentence that matched my intentions. (Fortunately and through much revision by Caitlin and our research advisor, the sections I wrote have changed beyond recognition).
Overall, I'm satisfied with the results of this project. While I do not have an immediate desire to start a new project (or to continue living like this for a five year PhD program), I do have a deeper appreciation for computing research. I hope that the next time I do resesearch, I know better what to do, can make fast progress, and can easily write about what I've done.
Cheers!
MaryAnn
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 processing my thoughts about them, and building a program and then talking about it. Certainly, both of these have required some amount of hypothesis testing, but the goal seems more to be of convincing others that my thoughts on a problem are valid and less on experiment-driven data. Since we did not complete our work on fairness in ranking, I would imagine this experiment side also exists but that we didn't quite get to it.
Reading back on my posts, it's frustrating to see how long it took to make progress. With regards to fairness, we had a basic idea of what we wanted to do, but the methodology didn't exist for the ranking context. I feel pretty happy now with how we've framed the problem, but we spent a long time thinking about fairness in classification and how to transform it to ranking.
I think one big difference in this project and my schoolwork is the lack of well-defined tasks. In my classes, I am used to being given an assignment where, even if the assignment is purposefully vague, the professors and teaching assistants have a good idea what needs to be done and unconsciously give hints as to good work strategies. In this project, some objectives took much longer to complete while others were relatively easy. For example, it was incredibly difficult to find datasets on which to evaluate fairness because we needed some truth variable. On the other hand, implementing isotonic regression to correct for fairness was a one-liner in Python. Much time was spent thinking about what to do next, but looking back makes all the tasks look deceptively straight-forward.
Writing is hard. I have grown to appreciate these blogs because they force me to write about my thoughts. While I don't believe they're very well written, it has at the very least been a good exercise for me to reflect on the past week. I plan to continue writing a blog with future research projects. Writing the paper was even more hard because I knew someone would eventually read it. It was challenging for me to explain my thoughts in coherent sentences, and I kept falling into a pattern of repeating myself or using the same words. However, it was very satisfying when I occasionally did write a sentence that matched my intentions. (Fortunately and through much revision by Caitlin and our research advisor, the sections I wrote have changed beyond recognition).
Overall, I'm satisfied with the results of this project. While I do not have an immediate desire to start a new project (or to continue living like this for a five year PhD program), I do have a deeper appreciation for computing research. I hope that the next time I do resesearch, I know better what to do, can make fast progress, and can easily write about what I've done.
Cheers!
MaryAnn
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