Published:
Though data about the federal judicial court system in the US is supposed to be open, in reality it is hard to access due to various friction points. Not only is there a paywall to access the data, but the information in that data is difficult to access due to the high barrier to entry for all but legal experts.
Published:
The focus on making information available to the public has largely been focused on access to data. However, access to raw data is not equivalent to access to information. There are still numerous obstacles for information accessibility even once the data is available, largely stemming from the lack of data science expertise in the general public.
Published in ICAIL, 2021
This paper is about automating data analytics for legal documents
Recommended citation: Paley, Andrew, et al. "From data to information: automating data science to explore the US court system." Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law. 2021.
Undergraduate course, University of Michigan, 2019
During my last semester as an undergraduate at the University of Michigan, I was the lead instructional aide for the Introduction to Machine Learning (EECS 445) course. In addition to helping the instructor supervise the teaching staff and manage logistic planning, I sided with planning course content, writing assignments, designing exams, and overall logistic support. I also:
Workshop, UNAM, C3, 2019
In RIIAA 2019, I instructed (primarily in Spanish) a summer school workshop on Recommender Systems, focusing on Machine Learning techniques and algorithm implementations. I also aided in the reviewing process for selecting the posters that would be accepted in the student poster competition.
Mentorship, University of Michigan, 2019
For the winter semester of 2020, I helped mentor two undergraduate students from the University of Michigan as part of the “Explore Computer Science Research” program that aims to improve diversity in CS research. Along with Prof. Sindhu Kutty (University of Michigan), I helped these students explore fairness in Machine Learning (ML) and how fair are recidivism prediction instruments. I also:
Undergraduate course, Northwestern University, 2020
As the main TA of this course, I helped the logistics and operations of the course by handling all the grading as well as supporting students via office hours. The most rewarding aspect of this course was getting to help students one-on-one who are just getting started in their Computer Science education. In a virtual year due to the pandemic, helping students further connected me to campus and to people in my field.