In July 2024, we published our AI Framework, a first attempt at setting guidelines on how we utilise AI in our work at mySociety. The basic principles running through that framework can be summed up as “Use AI responsibly, and only when, all things considered, it is the best tool for the job”.
That position will serve us, and any organisation, for life — but, with such a fast-moving field and such speedy integration across so many of our areas of work, the consideration of ‘all things’ must happen at regular intervals. It’s important that we keep checking in, to ensure that the framework is still providing timely and relevant guidance that reflects the current circumstances.
With this in mind, we’ve implemented a three-pronged approach:
- The AI Framework itself: this basic set of principles and questions is designed to act as a resource that staff members can keep referring back to if in doubt — and is a living document which we regularly update to reflect these fast-moving times.
- Our AI register: an internal spreadsheet, where staff are required to note new uses of AI, so that we have an up to date picture of how it is being deployed in areas across the organisation. This has already collected diverse use cases, from generating the transcripts of our podcasts with third party AI-based tools, to our own experiments with LLMs, like detecting inappropriate use of WhatDoTheyKnow. Where we think there are learnings for others to benefit from, we’ve written up these use cases here on our blog.
- A regular ‘Generative AI and Machine Learning’ meeting: this gives us the opportunity to discuss entries on the register, ask questions and consider any issues that have arisen.
So, with all this analysis going on, what are the changes we’ve seen since the first iteration of the framework? As you might imagine, they’re far-reaching, both in our own practices and in the wider world.
In our development: There’s been increasing experimentation and use of coding agent tools from our developers, always with an eye to whether these are producing valid outputs that genuinely save time, or solve problems that other tools couldn’t.
Some of the datasets we create now rely on LLM processes, where flexibility in interpreting and transforming language helps us create and combine data in ways not possible through other methods. These include the collection of data on APPGs; and our WriteToThem Insights work.
We’ve experimented with some AI-based problem-solving on our websites and infrastructure: for example, screening for personal immigration requests that have mistakenly been submitted on WhatDoTheyKnow (a longstanding issue); and we are in the early stages of exploring machine learning approaches to help us understand potential ways of handling any abusive messages sent through WriteToThem.
As for the external landscape: increasingly, we’re seeing funding opportunities that centre around the responsible deployment of AI for the good of democracy or transparency; and in our international community, via our Communities of Practice and TICTeC, we’re hearing of both more and more innovative application of AI to civic tech tools; and ways of monitoring its use by the state.
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Image: Steve Johnson
