This past week, I was at a pair of workshops on Workshop on Fairness, Accountability, and Transparency in Machine Learning and Data and Algorithm Transparency. They were both great workshops.

For obvious reasons, the election hung as something of a cloud over the meetings. It wasn’t constantly discussed, but we kept returning to it from time to time. It’s pretty sad, in my opinion, when ‘what does this work like if rule of law collapses?’ is a live question. Regulation is a key outcome of fairness research, and representatives from a number of regulatory agencies were in attendance. There’s a very real concern that regulation and policy will not be available levers for the next several years.

Some of the discussion, therefore, was about ways to supplement or compensate for lack of regulatory mechanisms. Far more questions were raised than answered, I think, but it was discussed both in the panels and in hallway discussions.

As we were talking about this, I couldn’t help but think of Bruce Schneier’s Liars and Outliers. I think this book provides a very helpful framework and language for reasoning about what, exactly, we might be trying to do as we promote fairness and nondiscrimination.

Inspired by Philip Guo’s post, here are the application materials I submitted in the course of my two computer science faculty job searches.

I am posting these in the hopes that having more examples available helps some job applications. However, it’s important to note that there is not a formula you can — or should — follow slavishly for these documents. When I took Preparing Future Faculty at UMN, our instructor encouraged us not to read other teaching statements before we wrote our own, so that our statements came from us. I don’t know that you need to go that far, but your teaching and research statements should reflect you as a teacher and scholar.

Shriram Krishnamurthi at Brown wrote today about his pivot into CS education research. I found the whole article fascinating to read, but found this paragraph at the end intriguing as I am thinking about my research agenda over the next few years:

When I became a professor, I decided it would be good to take on “five-year plans”: pick a topic and work with it for about five years (with a trailing year or two to disseminate results). That’s long enough to really get into its guts, understand it at depth, make real contributions, but also not become stale. (And most of all, not become too attached to my insider status, which would breed conservatively sticking to it and cranking out papers with rapidly diminishing returns.) I’ve done that now on five projects, and it’s worked well for me. This would be my sixth five-year project. In some ways, this is the most terrifying one because it’s the one I’m least prepared for: when I read @markguzdial’s writings, I feel I’m not just a novice, I’m trying to reach a different planet. But I’ve muddled through before, and I’m excited to try doing so again.

We talk about 5-year plans a lot in academia; forming one is common advice to new faculty. But I think this is the first time that I have seen the suggestion of thinking of a career as a sequence of 5-year plans that don’t necessarily all focus on the same agenda.

I’ll be thinking about this as I work on my 5-year plan.

I’m looking forward to going back to RecSys this year, reconnecting with old colleagues and meeting some new ones.

I also am involved with a couple of papers this year and will be presenting one, as well as serving as publicity co-chair.

IN RE the Jello Research Challenge:

Sometimes people need directions when making jello.

But there are so many ways to prepare jello, and different directions for different dishes.

What if your dishes could track what you’ve done with them and use that information to help you find more relevant jello instructions?