Machine learning in the abstract.
I'm coming up for a bit of air after spending the first half of this semester talking to everyone who would talk to me and reading everything I could get my hands on about fairness and accountability in automated decisionmaking systems. Luckily, the transition period between that research and the paper-drafting phase coincided with two great conferences—the Conference on Fairness, Accountability, and Transparency (FAT*) and Princeton CITP's AI and Ethics conference. This was my first foray into academic conferences in this space, and both were energizing.
Overall, I was struck by how many strong, smart young people are leading the field or are interested in it. I watched the FAT* livestream and didn't attend in person, but heard that a large portion of the audience consisted of students, in addition to the youthful presenters. At CITP, too, a number of both panelists and audience members were young. That's inspiring.
I was also generally impressed by the openness of people to dialogue and criticism, with the goal of moving the ball forward for the benefit of society. Lofty goals, but there were also a lot of people clearly getting s*** done.
The presentations I heard and the conversations I had gave me a sense that I'm on a good track with my current line of research. In my final independent study at Georgetown (I did two of them with two different products instead of one thesis), I'm mapping out the life cycle of machine learning classification systems, potential intervention points for regulation/oversight, and the stakeholders involved. This grew out of a desire to better understand the state of research in this space and what can actually be done in practice. I hope the final product will be useful both to those just entering the automated decisionmaking/AI/machine learning/big data governance/accountability/fairness/ethics area of focus and to those who have been around for a while. (More on sorting out that mess of terminology later on.)
The process is a bit frustrating, as I know that this is a rapidly evolving area, and I'm only biting off a chunk. That's why I'm focusing my work on a high, systems level. What I produce is going to end up raising more questions than it answers, and I regret that I don't have more time as a student to pursue some of those lines of research further. But hopefully, with this roadmap, I'll be able to shed some light on necessary areas of work for others and conduct some of it on my own in a different capacity.
For now, it's back to outlining!