News and views
Hello, and welcome to our 25th Data Policy Digest, bringing you all the latest data and AI policy developments.
Ah, autumn. Season of mists and mellow fruitfulness - and unhealthy, underwhelming beige buffet bites and warm white wine. Yes, several of us are heading up to Liverpool for Labour Party conference this weekend - if you are too, check out our spreadsheet of data and AI events, or drop me a line if you’d like to meet up.
Hello, and welcome to our 24th Data Policy Digest, bringing you all the latest data and AI policy developments, with something of a back-to-school feel - though it’s not as if, for those of us following data and AI policy, it feels like there was much of a break at all.
Hello, and welcome to our 23rd Data Policy Digest, bringing you all the latest data and AI policy developments.
There have been a lot. But you knew that already. Because there always is. And will no doubt continue to be. Now, and forevermore. Amen.
Opinion
For a researcher who has spent a little time in the Ivory Tower, and then tried hard to find a career path escaping it, during my time at Connected by Data has been inspiring to see practical examples of how public engagement can shape fairer, more accountable systems of data and AI governance. I have to give a shout out to my favourite design lab and my favourite connected conversation which both shared different approaches to including communities in Ai decision making. This is work I hope to be doing more of, more directly, in the future.
The Department for Education has recently released public attitudes research on what parents and pupils think about AI in education, as part of its announcement of a £4m investment to create a dataset to support building AI tools. This is a bit of a hangover from the previous government (the work was carried out earlier in 2024), but reflective of the current government’s commitment to maximising adoption of AI across the public sector.
Before I dig into the details, I should first say that it’s fantastic to see public sector organisations carrying out public attitudes research to inform how they approach the adoption of AI. This kind of research can be used to prioritise investments, inform governance processes to address anticipated harms, and identify barriers and blockers to adoption, as well as working out how to communicate about governmental plans.
Here I want to pull out some specific insights from the research that highlight considerations for how technology is rolled out for public services, namely about profit sharing; schools as trusted decision makers; and points about equity and choice. Then I’m going to discuss some lessons that should be taken into future similar public engagement exercises, particularly about shifting understanding and acceptance of technology; consulting teachers and workers; and the overall approach we need of “you said, we listened”.
I posted recently about the challenge of adoption of AI by the public sector.
There are two sides to this: how the public sector adopts AI itself, and how the public adopts AI-driven public services.
I’ve posted recently about the challenge of purpose and priorities in the adoption of AI by the public sector. This blog post expands on this to look not at what I think the priorities should be, but about how they should be decided, and that prioritisation method institutionalised given it’s not a one off exercise.
Weeknotes
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