-
/RSS FeedIt’s our first ever podcast at mySociety! Heeey how about that?
Myf, our Communications Manager, runs you through all the stuff we’ve been doing at mySociety over the last month. It’s amazing what we manage to fit into just 30 days: you’ll hear about a meeting of Freedom of Information practitioners from around Europe; our new (and evolving) policy on the use of AI; a chat with someone who used the Climate Scorecards tool to springboard into further climate action… oh, and there’s just the small matter of the General Election here in the UK, which involved some crafty tweaking behind the scenes of our sites TheyWorkForYou and WriteToThem.
Links
- TICTeC videos on YouTube
- TICTeC photos on Flickr
- Browse the TICTeC 2024 schedule, find slides etc
- Matthew’s post on updating TheyWorkForYou on election night
- Sign up to get an email whenever your MP speaks or votes
- Democracy resources and our future plans in Alex’s post
- Local Intelligence Hub lets you access and play with data around your constituency
- Matt Stempeck’s summary of the Access to Information meetup
- Our summary of Matt’s summary of the meetup
- Updates from all those ATI projects around Europe
- New in Alaveteli: importing & presenting blog posts; request categories and exploring csvs in Datasette
- Fiona Dyer on how volunteering for Scorecards upped her climate action
- Where to sign up if you fancy volunteering as well
- mySociety’s approach to AI
- Contact us on hello [at] mysociety.org if you have any questions or feedback.
Music: Chafftop by Blue Dot Sessions.
Transcript
0:00
Well, hello and welcome to mySociety’s monthly round-up.
My name is Myf Nixon, Communications Manager at mySociety.
0:11
This is part of an experiment that we’re currently running where we’re trying to talk about our work in new formats, to see if that makes it easier for you to keep up with our news. (more…)
-
To react appropriately to the emergence of AI, we need to understand it. We’re making our internal AI Framework public as a way of being transparent about the kind of questions we’re asking ourselves about using AI in mySociety’s tools and services.
At our recent TICTeC conference, there were several great examples of how generative AI approaches can be applied to civic tech problems. But regardless of whether civic tech projects use AI approaches directly, it’s increasingly part of the tools we use, and the context our services exist in is being changed by it.
A key way mySociety works is by applying relatively mature technology (like sending emails) in interesting ways to societal problems (reporting problems to the right level of government; transforming Parliamentary publishing; building a massive archive of Freedom of Information requests, etc). This informs how we adapt and advance our technical approach – we want to have clear eyes on the problems we want to solve rather than the tools we want to use.
In this respect, generative AI is something new, but also something familiar. It’s a tool: it’s good at some things, not good at other things — and, as with other transformative tech we’ve lived through, we need to understand it and develop new skills to understand how to correctly apply it to the problems we’re trying to solve.
We currently have some funding from the Patrick J. McGovern Foundation where we’re exploring how new and old approaches can be applied to specific problems in our long running services. Across our different streams of work, we’ve been doing experiments and making practical use of generative AI tools, working with others to understand the potential, and thinking about the implications of integrating a new kind of technology into our work.
Our basic answer to “when should we use AI?” is straightforward. We should use AI solutions when they are the best way of solving problems, are compatible with our wider ethical principles and reputation, and can be sustainably integrated into our work.
Breaking this down further led us to questions in six different domains:
- Practical – does it solve a real problem for us or our users?
- Societal – does it plausibly result in the kind of social change we want, and have we mitigated change we don’t want?
- Legal/ethical – does our use of the tools match up to our wider standards and obligations?
- Reputational – does using this harm how others view us or our services?
- Infrastructural – have we properly considered the costs and benefits over time?
- Environmental – have we specifically accounted for environmental costs?
You can read the full document to see how we break this down further; but this is consciously a discussion starter rather than a checklist.
Publishing this framework is similarly meant to be a start to a discussion — and an anchor around open discussion of what we’ve been learning from our internal experiments.
We want to write a bit more in the open about the experiments we’ve been doing, where we see potential, where we see concerns. But this is all just part of the question at the root of our work: how can we use technology as a lever to help people to take part in and change society.
—
Image: Eric Krull
-
Artificial intelligence and machine learning seem to be everywhere at the moment – every day there’s a new story about the latest smart assistant, self-driving car or the impending take over of the world by robots. With FixMyStreet having recently reached one million reports, I started wondering what kind of fun things could be done with that dataset.
Inspired by a recent post that generated UK place names using a neural network, I thought I’d dip my toes in the deep learning sea and apply the same technique to FixMyStreet reports. Predictably enough the results are a bit weird.
I took the titles from all the public reports on fixmystreet.com as the training data, and left the training process to run overnight. The number crunching was pretty slow and the calculations had barely reached 5% in the morning. I suspect the training set was a bit too large, at over 1M entries, but end result still gives enough to work with.
The training process produces checkpoints along the way, which you can use to see how the learning is progressing. After 1000 iterations the model was starting to be aware that it should use words, but didn’t really know how to spell them:
Mertricolbes Ice does thrown campryings Sunky riking proper, badger verwappefing cars off uping is! Finst Knmp Lyghimes Jn fence Moadle bridge is one descemjop
After 15000 iterations it’s starting to get the hang of real words, though still struggling to form coherent sentences.
Untaxed cacistance. Broken Surface in ARRUIGARDUR. Widdy movering Cracked already nail some house height avenue. Light not worky I large pot hole Dumped shood road nod at street. Grim Dog man Ongorently obstructing sofas. This birgs. Serious Dirches
After 68000 iterations there seems to be enough confusion in the training data that things start to go south again with the default parameters:
Urgely councille at jnc swept arobley men. They whention to public bend to street? For traffic light not working
Tweaking the ‘temperature’ of the sampling process produces increasingly sensible results:
Large crumbling on pavement Potholes all overgrown for deep pothole Very van causing the road Very deep potholes on pavement Weeds on the pavement Several potholes in the road Rubbish Dumped on the road markings Potholes on three away surface blocking my peride garden of the pavement Potholes and rubbish bags on pavement Poor road sign damaged Poor street lights not working Dog mess in can on road bollard on pavement A large potholes and street light post in middle of road
As well as plenty of variations on the most popular titles:
Pot hole Pot hole on pavement Pot holes and pavement around Pot holes needings to path Pothole Pothole dark Pothole in road Pothole/Damaged to to weeks Potholes Potholes all overgrown for deep pothole Potholes in Cavation Close Potholes in lamp post Out Potholes in right stop lines sign Potholes on Knothendabout Street Light Street Lighting Street light Street light fence the entranch to Parver close Street light not working Street light not working develter Street light out opposite 82/00 Tood Street lights Street lights not working in manham wall post Street lights on path Street lights out
It also seems to do quite well at making up road names that don’t exist in any of the original reports (or in reality):
Street Light Out - 605 Ridington Road Signs left on qualing Road, Leave SE2234 4 Phiphest Park Road Hasnyleys Rd Apton flytipping on Willour Lane The road U6!
Here are a few of my favourites for their sheer absurdity:
Huge pothole signs Lack of rubbish Wheelie car Keep Potholes Mattress left on cars Ant flat in the middle of road Flytipping goon! Pothole on the trees Abandoned rubbish in lane approaching badger toward Way ockgatton trees Overgrown bush Is broken - life of the road. Poo car Road missing Missing dog fouling - under traffic lights
Aside from perhaps generating realistic-looking reports for demo/development sites I don’t know if this has any practical application for FixMyStreet, but it was fun to see what kind of thing is possible with not much work.
—
Image: Scott Lynch (CC by/2.0)
