Monthly Archives: November 2018

Will Code for Drinks @ Scrollbar 2018

Homemade poster for the event

I am foremost a teacher, and I care a lot about introductory programming, computational thinking, algorithms, and the social environment of a university.

As an experiment in “social coding,” we implemented an event called “Will Code for Drinks @ Scrollbar” in the Friday bar at IT University of Copenhagen on 23 November 2018. The basic idea is to get beginning programmers—this includes 1st semester students and professors—together for a few hours, solve some well-defined programming exercises, and get a drink for each solved exercise.

Preparation

The idea was born over a couple of lunches with colleagues, and thanks to the enthusiasm of Martin Aumüller and Troels Lund it quickly developed momentum.

The platform we used for this is Kattis (open.kattis.com), which is a well-working system developed for programming competitions. Kattis comes with thousands of extremely well-done exercises, a reliable server with an accessible web interface, and very simple procedures for registering individuals, forming teams, and hosting contests.

Preparation included:

  1. Establishing rapport with the ITU Friday bar—a student-run organisation that hosts a social event every Friday afternoon during the semester. They loved the idea, and we were able to find a free date where no other ScrollBar event or theme (Halloween) was already planned, and no other ITU event (Board Game night!) was scheduled elsewhere in the house by one of the many other social committees.
  2. Learning Kattis and solving dozens of exercises there in order to find a selection of the event. The idea was that students with very little programming experience were supposed to at least solve one exercise, possibly with help, during the event. The selection we ended up with were Baby Bites, Spavanac, the lovely Trik, and the somewhat harder Bank Queue. We figured that 3 drinks are enough for anybody, and wanted a more challenging problem to keep everybody engaged during the event.
  3. Spreading the word among first-year programming teachers and their students. During the weeks up the event, our enthusiasm infected several students, who registered on Kattis and started grinding in preparation.
  4. Decisions, decisions… should the event be called “Will Code for Beer” instead? Drink or drinks? How competitive should we make it?
  5. Creating a visual identity. My original plan was to use a lot of images of various people holding cardboard “Will Code for Drinks” signs. In the end, it was too much work (after talking to the communications department about GDPR), and I cobbled together a clean visual identity on my computer in a couple of minutes. Another reason to reject the hobo-theme is that it possibly repels students who are concerned about appearances.
  6. Find some way to pay for the resulting bar tab, and that will be acceptable to the Accounting and Finances section.
  7. Design and order caps so that assistants (Martin, Troels, and myself) would be visible during the contest. Alas, the caps were not delivered on time.
An early experiment in developing a visual identity for the event. In the end, I rejected this direction, despite the great resonance among students and colleagues, for purely aesthetic reasons.

During the event

The event was “just” a contest on the open Kattis server  https://open.kattis.com/contests/f4ktq9

The moment the contest started at 15:30, we had most of the students in the same room adjacent to the bar, so we could help with Kattis registration, logging in, reading from standard input, etc. After that, participants slowly moved into ScrollBar and the ITU Atrium. We kept ourselves visible and available, and helped with programming and problem solving.

After

I spent a few hours writing emails to individual groups that I had talked to during the contest, explaining other approaches to specific tasks. Then I sent a brief thank-you note to all participants that I could identify and invited feed-back and suggestions for improvement. This was quite boring, I had to identify partipants  who had registered under their own name on Kattis and had a name I could uniquely find in the ITU student roster.

Evaluation

This was supposed to be a test run, and I had hoped for 5 teams of students. In reality, slightly over 50 teams registered, with 130 participants. Stunning success!

Will Code for Drinks @ ScrollBar 2018 in full activity. Photo by Troels Lund.

Of the participants I was able to identify, 48 are first-semester students. These were the intended target group. More that half of the students are from the educations hosted by the Computer Science department, but all of ITU’s student populations were present. 45 teams solved at least one problem, 35 teams solved three. 10 teams solved all four problems; this includes the teams consisting of faculty members and Ph.D. students. Phew!

In the end, the “damage” was 183 beers, 80 cocktails, and 8 soft drinks. In total, students solved 132 programming exercises in 2.5 hours, and fun was had. As a teacher, I couldn’t be happier.

In just a few weeks, ITU is now the second-largest and second-ranked Danish uni on Kattis. Aarhus is still way ahead.

Future

I would love to make this event even more social and less competitive. An idea that came up during the contest was to have the scoreboard ranked by “most recent solve” rather than “number of solves”. That way, every team gets to be at the top at least once. Removing the scoreboard entirely is another option, but that removes the shared digital forum – in effect, all the teams would exist in their own little bubble.

The best idea we’ve come up with in this vein is to couple the teams with music playlists. Then the current leader (i.e., the team that most recently solved a problem) would decide which music is played in the bar. “Will Code for Drinks and Music” or “Will Code for Drinks and Rick Roll” or something. To make this work, we need a more advanced registration system, and we’d need to scrape the standings off the Kattis server.  All doable.

Another improvement would be to have our own, ITU- or ScrollBar-branded problems instead of relying on (often well-known) problems from the Kattis pool. We could switch to another system than Kattis (or build our own) but that is a lot of work, and there is intrinsic value in incentivising students to register on Kattis.

No matter the form, we will certainly do this again in Spring 2019!

A Glimpse of Algorithmic Fairness

Workshop presentation at Ethical, legal & social consequences of artificial intelligence, Network for Artificial Intelligence and Machine Learning at Lund University (AIML@LU), Lund University, 22 November 2018.

Abstract

Several recent results in algorithms address questions of algorithmic fairness — how can fairness be axiomatised and measured, to which extent can bias in data capture or decision making be identified and remedied, how can different conceptualisations of fairness be aligned, which ones can be simultaneously satisfied. What can be done, and what are the logical and computational limits?

I give a very brief overview of some recent results in the field aimed at an audience assumed to be innocent of algorithmic thinking. The presentation includes a brief description of the location of the field algorithms among other disciplines, and the mindset of algorithmic or computational thinking. The talk includes pretty shapes that move about in order to communicate some intuition about the results, but is otherwise unapologetic about the fact that the arguments are ultimately formal and precise, which is important for addressing fairness in a transparent and accountable fashion.

References

Toon Calders, Sicco Verwer: Three naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Discov. 21(2): 277-292 (2010). [PDF at author web page]

Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. [arXiv 1703.00056]

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel:
Fairness through awareness. Innovations in Theoretical Computer Science 2012: 214-226. [arXiv 1104:3913]

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, Suresh Venkatasubramanian: Certifying and Removing Disparate Impact. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015. [arXiv 1412.3756]

Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian: On the (im)possibility of fairness. [arXiv:1609.07236]

Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum: Multicalibration: Calibration for the (Computationally-Identifiable) Masses. Int. Conf. Machine Learning 2018: 1944-1953. [Proceedings PDF]

Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan: Inherent Trade-Offs in the Fair Determination of Risk Scores. Innovations in Theoretical Computer Science 2017: 43:1-43:23. [arXiv 1609:05807]


(The image at the top, the title slide of my presentation, shows a masterpiece of the early Renaissance, Fra Angelico’s The Last Judgement (ca. 1430), illustrating a binary classifier with perfect data access and unlimited computational power.)