Let’s go back to the start of AI and Regulation. In November 2022 we had ChatGPT launch, which caused a load of hype and interest in AI. Let’s step through some of the important UK government reports about AI which could have an impact on social housing providers.

UK government’s AI Regulation White Paper of August 3, 2023 (the “White Paper”) and its written response of February 6, 2024 were perhaps the first position papers. The key thing here? There likely won’t be a central regulator for AI. Some government-recommended principles for all Regulators:

• Principle 1: Regulators should ensure that AI systems function in a robust, secure, and safe way throughout the AI life cycle, and that risks are continually identified, assessed and managed. 
• Principle 2: Regulators should ensure that AI systems are appropriately transparent and explainable.
• Principle 3: Regulators should ensure that AI systems are fair (i.e., they do not undermine the legal rights of individuals or organizations, discriminate unfairly against individuals, or create unfair market outcomes).
• Principle 4: Regulators should ensure there are governance measures in place to allow for effective oversight of the supply and use of AI systems, with clear lines of accountability across the AI life cycle
• Principle 5: Regulators should ensure that users, impacted third parties and actors in the AI life cycle are able to contest an AI decision or outcome that is harmful or creates a material risk of harm, and access suitable redress.

Next we have the AI opportunities action plan (Jan 2025). This positions the UK government’s approach to AI, and was much promoted in the media. In terms of Housing, there are a few key recommendations: That the government “Commit to funding regulators to scale up their AI capabilities, some of which need urgent addressing”, and potentially the most exciting suggestion for Housing is “Require all regulators to publish annually how they have enabled innovation and growth driven by AI in their sector.”. The report also contains an implementation framework, suggesting an approach to AI is: Scan > Pilot > Scale. I’d say Housing as a sector is somewhere between Scan and Pilot at present, and varying wildly by individual organisation. An interesting part of this will be the Regulator for Social Housing’s future position on AI for the sector – something not yet published (many Regulators haven’t, this isn’t odd).

Another newsworthy article, in Feb 2025 the UK Government declined to sign the Statement on Inclusive and Sustainable Artificial Intelligence at the Paris AI Summit. Signatories to the Declaration pledged to make AI “open, inclusive, transparent, ethical, safe, secure, and trustworthy”. I don’t think this directly impacts Social Housing too much but sets some of the trajectory for wider commercial developments.

Quietly, an Artificial Intelligence Playbook for the UK Government was published in Feb 2025. This report contains decent advice for AI in public services. In my view these also hold well for Social Housing providers. The paper is short and worth a read. Key considerations they outline:

1. You know what AI is and what its limitations are.
2. You use AI lawfully, ethically, and responsibly.
3. You know how to use AI securely.
4. You have meaningful human control at the right stage.
5. You understand how to manage the AI life cycle.
6. You use the right tool for the job.
7. You are open and collaborative.
8. You work with commercial colleagues from the start.
9. You have the skills and expertise needed to implement and use AI.
10. You use these principles alongside your organisation’s policies and have the right assurance in place.

The EU AI Act came into force in Feb 2025: This is a consideration for Housing, in that this thinking pattern may be adopted in the UK. It is quite large, to the extent it has its own navigation software for all the chapters, annexes, and recitals. An important (and in my view sensible) thing is having different risk levels with differing scrutiny required. 

I’ll just call out one level: “Unacceptable risk, therefore prohibited, examples include the use of real-time remote biometric identification in public spaces or social scoring systems, as well as the use of subliminal influencing techniques which exploit vulnerabilities of specific groups.”. Housing providers need to act ethically and responsibly, and use of data systems including AI is no exception. 

Artificial Intelligence (Regulation) Bill (2025), a private member’s bill passed first reading in the House of Lords 2 weeks ago. It seems unlikely to pass, but again may signal the future trajectory for AI regulation. According to Kennedy’s, the Bill suggests: 

1. Creation of an AI Authority: the Bill proposes the establishment of a dedicated regulatory body tasked with overseeing AI compliance and coordinating with sector-specific regulators.
2. Regulatory Principles: The Bill enshrines the Five AI principles, derived from the UK government’s March 2023 white paper, “A Pro-Innovation Approach to AI Regulation.”
3. Public Engagement and AI Ethics: The Bill highlights the need for public consultation regarding AI risks and transparency in third-party data usage, including requirements for obtaining informed consent when using AI training datasets.

Final thoughts: Don’t forget, we still have GDPR. Compliance with GDPR in all data systems remains a requirement. In addition, the risk of GDPR breaches has definitely increased as a result of genAI. Anecdotally, we’ve seen things like customer information being emailed out to personal accounts because ChatGPT is blocked on the network. Firming up on GDPR and on Cyber Security is always important. Keeping abreast of the latest regulatory and ethical considerations is also a good idea at this time, whilst also better understanding and achieving benefits from the tools we now have access to.

Written by a human who is not a lawyer. This is not legal advice.

You might have heard of structuralism in linguistics? The general idea, according to wikipedia, is that “elements of human culture must be understood by way of their relationship to a broader system.” This idea resonates with me as an agile practitioner and systems thinker, so I decided to explore diagrams more through this lens. It gets a bit deep, so I’ve made a video about it:

Exec summary

We can examine diagrams as if they are a language, and use this to uncover patterns and good practices.

Abstract: The efforts to contain SARS-CoV-2 and reduce the impact of COVID-19 have been supported by Test, Trace and Isolate (TTI) systems in many settings, including the United Kingdom. The mathematical models underlying policy decisions about TTI make assumptions about behaviour in the context of a rapidly unfolding and changeable emergency. This study investigates the reported behaviours of UK citizens in July 2021, assesses them against how a set of TTI processes are conceptualised and represented in models and then interprets the findings with modellers who have been contributing evidence to TTI policy. We report on testing practices, including the uses of and trust in different types of testing, and the challenges of testing and isolating faced by different demographic groups. The study demonstrates the potential of input from members of the public to benefit the modelling process, from guiding the choice of research questions, influencing choice of model structure, informing parameter ranges and validating or challenging assumptions, to highlighting where model assumptions are reasonable or where their poor reflection of practice might lead to uninformative results. We conclude that deeper engagement with members of the public should be integrated at regular stages of public health intervention modelling.

Link: https://www.medrxiv.org/content/10.1101/2022.01.31.22269871v1

This week, we’re shaking things up. Instead of the usual weekly blog, we have prepared a special something, which we will release tomorrow.

Stay tuned for more details!

This post is inspired by a question from one of our readers, Lukasz. I’m going to outline how I find and examine research on organisations, agile/lean, and culture. Hopefully this will inspire you to dig more into what stuff is true and what is just crap.

Finding the genuine facts among the huge volume of opinion is hard. It’s hard in politics, it’s hard in management, and it’s hard in social science. As a mathematician, I come from a world things are either true or not, and I continue to find exploring ambiguous and opinion-rife research challenging.

Finding an interesting topic

First you need to know what you want to know. Inspiration for what to research can be found in case studies, papers, blogs, books, conversations, your own experience etc. I personally find my ways of thinking most easily challenged by experience, books, videos, and conferences (probably because these are accessible!).

Finding the research

Once you’ve something you want to know, and the vocabulary to describe it, I’d recommend googling with specific terms. For example, if you are interested in the impact of management on team members, I’d recommend something like: “role hierarchy team impact” or something similar. Stay away from buzzwords like “management” or “agile”.

Google scholar is good for finding paper titles, but often due to publishers you will have to pay for them. Knowing the title, if you search again specifically for those papers/authors you can often find a free version on the author’s academic page, or at least some related content.

Assessing research quality

1. Be cynical. Assume everyone is lying and check their “facts”.
2. Beware sweeping statements. It is hard to have good social science that is very general.
3. Use your noggin. E.G. Is the sample size big enough? Have they got a control group?

Beware research fashion

Just because something is popular to talk about (or highly cited) doesn’t make it good. A good example is Myers Briggs Type Indicators. Yes it is popular, and arguably helpful to some, but that doesn’t make it true or “the way to classify people”. Similarly some leadership styles are more heavily researched than others. The weight of research can be tempting to give in to, but keep sifting through, especially when the research is about models to help understand a topic (rather than an absolute truth).

Finally

Once you’ve something you think looks solid, a good test is to try it yourself! Run an experiment relevant to your situation, and see if you get results in line with the theory. Then tell other people what you’ve learned. (Yes I’m ignoring confirmation bias etc.)

If you have other techniques, or questions or suggested improvements to my ways of researching, please do share them in the comments!