Here's a white paper on 'Public AI Registers' by the CEO of the company (Saidot) that runs the service for both Amsterdam and Helsinki (co-authored with representatives of both cities):

https://uploads-ssl.webflow.com/5c8abedb10ed656ecfb65fd9/5f6...

Quite interesting to see how the word "algorithm" seems to be changing over time from a straightforward mathematical or CS "series of steps to solve a problem" to a general byword for any sort of automated reasoning, especially ones with a more malevolent undertone.

I'm actually kind of struggling to come up with a concise definition in the new popular usage.

Wow cool to see my country is doing this. is actually pretty awesome. Do other governments also do this? There is even an architecture breakdown on the Api that does the scanning of the license plate for ticketing.

https://algoritmeregister.amsterdam.nl/en/automated-parking-...

I tend to believe this is a valid approach for ML application in public institutions. Being ML a black box or a potential source of discrimination, setting up systems to ensure credibility within the public should be addressed early on in the design process (perhaps while designing the model itself).

I wouldn't be surprised to see a future where all ML applications affecting citizens and the general public interest (excluding defense, military, etc.) will be obliged to be open-source and subject to public scrutiny.

So great to see this discussion. When developing these registers, the target has been to enable citizen participation and public oversight of city algorithms. And to help cities and other government organizations to systematically govern their algorithmic systems. With that target in mind, conversations like this, anchored on proper data on the systems themselves, really count. Thank you! I'd love to invite everyone to give feedback also via our surveys at both sites.

Meeri / Saidot

Nice try, I like the gesture.

Count me extremely skeptical though. You can get decent to really good tabular learning right off the shelf and it‘s so good and ubiquitous by now that interestingly enough, a few things start to happen that imho might make this effort close to impossible:

- The differences between different algorithms get ever smaller, leaving more and more weight of any machine driven decision to the actual input data itself, including proper labeling, preparation, feature engineering, QA and debiasing. You can‘t properly separate the data from the algorithm itself anymore these days.

- Even IF you added all this data together with the algorithm in a register (which brings its own privacy and practicality concerns), with the massive breadth and width of modern data sources, most data scientists don‘t even understand model decisions by themselves anymore. You‘ll need to dig deep into the explainability toolbox to be able to at least partially explain them.

- There are dozens of documented cases where algorithms just mirror and regurgitate human biases through data. Algorithms by themselves are (mostly) innocuous, but can get to make racist, sexist and populist decisions anyway, purely based on what they have trained on.

So if you can‘t even trust the people who build the algorithms to understand their reasoning, what benefit could a publication possibly bring to any outsider?

Since it's just copied (or bought) from Helsinki (https://ai.hel.fi/en/ai-register/) / Saidot (Saidot.ai), I wonder if it's just something they bought but don't really care/understand what it's about or what they're putting there.

Regarding the automatic parking enforcement they claim, on the topic of non-discrimination:

> The service works the same way for all license plates regardless of the car model, age, or the owner’s profile.

This whole thing seems to be designed to give the appearance of government transparency without any of the transparency.

I bet if they released some "raw" (but anonymized) data from their collection it would be easy to find evidence of discrimination.

E.g. some of the richest parts of the city have some of the smallest parking spots. Surely cars bunched up against each other lowers the accuracy rate. Same with certain car models with recessed areas holding the number plate, which again will have socio-economic correlations. I don't see how you could plausibly design a system like this without any discrimination built into it.

But instead they just say there's no discrimination, without providing any of the source code, models or other data for the system. If they actually found that it was discriminatory does anyone think Amsterdam's first task would be to change its website to say "we're discriminating!", or would they just quietly try to fix it?

That's the issue with these sorts of systems. At least if one poor neighborhood is full of parking attendants with none in the adjacent rich neighborhood people will notice sooner than later. You have almost none of that basic sorts of transparency with automated scanning.

Nice to see my employer getting posted for this kind of stuff. In case you are interested we try to open-source as much as possible so check out our Github: https://github.com/amsterdam

Really interesting and useful way of displaying information in a very non government-y way

Nice to see this picked up! We're hiring technical people at the municipality, reach out to me via my profile if you're interested.

What is the intent of an algorithm register? And why Amsterdam? Isn't this more something that should be on an international level?