Notices by David Colarusso (colarusso@mastodon.social), page 2

Cygnet watch 2026 has official begun. Looks like early to mid June. 🤞

Two birds with one phone.

I turned right instead of left on my lunchtime walk only to discover Boston is being attacked by Kaiju. It’s not AI. It’s public art! https://www.winteractive.org

The rising temperatures have thawed my sketchbook. Last night’s sketch atop this morning’s walk.

2/ TL;DR: I built a bunch of highly-modular online simulations you can use with your students. They cover automation bias,¹ the false positive paradox,² competing definitions of fairness,³ disparate impact resulting from machine bias,⁴ and the value of due process.⁵

¹ https://www.davidcolarusso.com/42/citation_assistant/
² https://bail-risk-simulator-50382557550.us-west1.run.app/
³ https://fairness-simulator-the-toilet-seat-dilemma-50382557550.us-west1.run.app/
⁴ https://facial-recognition-bias-sim-50382557550.us-west1.run.app/
⁵ https://screening-vs-diagnostic-tests-50382557550.us-west1.run.app/

1/ There has never been a more concentrated distillation of my teaching than this lesson: Algos, Bias, Due Process, & You. It is the apotheosis of what I do. I very much hope you enjoy it, share it, and make bits of it your own. https://suffolklitlab.org/algos-bias-due-process-you/

5/ When that article was published I was a data scientist at the public defenders, and in my corner of the world, it created a bit of a furor, kicking up discussion around a set of issues I wanted students to understand. I thought, maybe they could role play as the court in a similar setup. So, I asked myself what students needed to understand to have an informed discussion, recognizing that I only had an hour and 50 minutes with them. 🤔

4/ The seed of the class was this reporting from 2016, "Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks." In it, the authors describe the use of a risk assessment tool by courts deciding questions like the granting of bail. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

3/ Most of them are self-explanatory. However, the first one intentionally lacks in-simulation context, and I think linking them together creates a more-compelling narrative arc. Play with the links in isolation (found in the above & in the blog post), or read the post for the guided tour. https://suffolklitlab.org/algos-bias-due-process-you/

9/ You’ll notice mention of a “Rival Clerk,” along with a reminder that we’re measuring the user’s speed et al. Dear reader, the “Rival Clerk” is not one of their peers. It’s a dark pattern⁶ designed to make them keep going. There’s so much in here ripe for discussion.

8/ I told them that for our first exercise they would all be using an AI assistant I built to review citations. After they had a chance to use it we would have a class discussion. I suggested they hold the following question in their head, “What makes something a good decision assistant?“

7/ It occurred to me that all they knew about my session was that it would be on “algorithmic bias.” What if I could get them to experience automation bias first-hand? That would make it harder to dismiss as something that only other people fall victim to. . . . A plan began to form.

6/ Here's what I came up with:
- accuracy isn't always the right performance measure
- mathematical models encode and replicate the biases found in their training data
- there can be competing and contradictory ideas of what makes something fair
- under certain conditions people are likely to over-rely on machine outputs (automation bias)
- the choices we make about how to use tools embody and reveal what we value

11/ Almost everyone fell victim to automation bias. The assistant's accuracy was 100% in phase 1 & 2, then dropped to 70%. Student performance started at 79% in phase 1, improved to 85% for a bit, but when the tool's accuracy declined, scores fell to 65%, worse than their initial performance.

10/ When they finished, users were shown a results screen that explained a bit more about the exercise. There were three possible outcomes: (1) No clear evidence of automation bias (2) You may have fallen victim to automation bias; and (3) You likely fell victim to automation bias.

7yo asked why I cut holes in the top, and I got to explain about what happens if you don’t. She took that in and remarked that they looked pretty too. Which was the perfect hook for me to agree and talk about symmetry. ;)

Pie is in the oven. Up next: the gingerbread house.

Working on a 7yo-proof definition of a clean room.

A room is clean when: the closet can close; the floor is walkable (i.e., nothing on the floor that isn't furniture, a rug, or a container);¹ laundry is in the hamper; & it is free of food & all other "growth media."²

¹ no fire hazard
² no ☣️

We visited a seasonal pop-up donut factory to get some pumpkins et al. Apple-cider donuts, yum.

The kids are playing a game. One is saying that the other is under arrest, and the one in custody is saying, “I won’t talk without my attorney.” At this moment I’m a very proud parent/former public defender.

I received the most delightful email this morning. It was a Google Scholar alert letting me know someone had cited my work, and for the first time ever, I recognized one of the authors’ names as a former student. 🥰