AI and the environment, beyond dystopia vs utopia

At TechforGoodSouthWest, we're passionate about building momentum for the TechforGood movement across the Southwest of the UK. Our mission is to amplify the positive impact of technology in tackling societal challenges and creating stronger, fairer and more connected communities. By fostering a regional TechforGood community, we aim to support initiatives that ensure technology serves a greater good and enable everyone to thrive in the long term. Throughout this podcast, we'll be joined by diverse range of voices from across the Tech sector, charities, investors, startups and community-driven projects, as they share their stories, challenges and hopes in harnessing TechforGood. Join us as we explore a world of TechforGood right here in the Southwest, brought to you by Annie, Alicia and Ariel. Welcome to the TechforGood Southwest podcast. I'm your host Ariel Ty, and today I'm joined by Mary Stevens, the Experiments Program Manager at Friends of the Earth. Welcome Mary. Hi, great to be here. Great to have you here. Mary's background spans public policy, social action and research, with a focus on systems thinking for environmental action and community led change. More recently, she's been exploring how AI could be used to advance environmental justice. Now we've been chatting about this hugely topical area, because while there's the potential of AI to drive positive change, there are also serious questions about its risks and impacts on the environment. So let's dive straight in, Mary. Please, if you could just tell me a bit more about your role and how you've been exploring this topic. Thanks. It's a big topic, isn't it, to dive straight into. So I've been at Friends of the Earth since 2018, and actually we've been thinking in my team, which is basically an innovation team. We're a very small team, but we've been thinking about this issue really since 2018, so for quite a long time now. And one of the reasons we do that is because we think it's really important for a campaign organization like Friends of the Earth to have a team that is looking a little bit beyond where the mainstream of the organization is looking. So thinking about the risks and opportunities that might be coming down the track, part of our work involves kind of horizon scanning and transporting. We see one of the things that was on the horizon even back in 2018 was the use of the applications of AI. And obviously since since 2022, roughly, this is kicked off again with the rise almost seemingly out of nowhere of kind of degenerative AI and large language models and so on. So we've been working, amongst other things, we've been working on this topic for quite a while. We had some funding, we were very lucky to secure some funding from something called the Green Screen Coalition via the Mozilla Foundation to do a bit of an in-depth dive into the relationship between AI and environmental justice and to think about some principles and practice recommendations for civil society, basically they're brought together these two issues. And that was a project that we did also in collaboration with the wonderful people at We Are Open, who we co-authored that piece with. So that's really been a springboard for thinking in a bit more depth around what these issues that you spoke about might mean for campaigning organisations like ourselves, smaller organisations, social and surprises, civil society organisations. Thank you and it's it is I think when we were talking before it's such an important topic that so many organisations that I'm working with are grappling with. And you mentioned the report that you put together. So it's called harnessing AI for environmental justice. And in there you talk about this narrative between AI will fix all of our problems or it's going to lead to societal collapse. There's this sort of utopian versus dystopian, I think you describe it as narrative that's going on. And that in your report you say that you aim to create a new story for AI that's somewhere in between this dystopia and utopia. Can you tell me more about this? Yes, I think this, you know, this polarity between sort of dystopian and utopian visions is something that we're actually all pretty familiar with. So I think this has changed a little bit but a couple of years ago when you were talking about when you started talking about AI, you know, the images that immediately came to mind was sort of you know the terminator and those kinds of things, you know, it was kind of AGI taking over the world and the robots in control and even, you know, and even when we had UK government talking about AI risk and so on, they kind of actually it felt like a lot of the risks that they seem to have in mind were around these kind of, you know, artificial general intelligence and the robot take over risks and, you know, lots of people, not just ourselves, but lots of people were saying, you know, back then actually, you know, this isn't about some kind of future risk. There was some very real existential risks involved in the role of AI for communities, you know, right now at the sharp end of that, whether that's at the sharp end of kind of mineral extraction, whether that's communities are experiencing kind of bias and discrimination through the application of AI systems that they're then, you know, and no accountability for that and so on. So, you know, so there's the dystopian side and then, you know, and then the flip side to that I think is that we've also seen a lot of narrative again, a narrative driven by, you know, a different facet of big tech that has been very much about. Now, AI is going to be brilliant because it's going to solve the climate crisis, it's going to cure cancer, it's going to do, you know, it's going to lead us to these kind of sunlit uplands where naturally what we see when you look into it a little bit more is that, you know, yes, there are some fantastic applications of AI for all sorts of them for these different research agendas that there are lots of ways in which, you know, AI is supporting, you know, climate modeling or medical research or whatever, but that actually only really represents a fraction of how the technology is being used, but it's a very, very useful, it's a very useful narrative for big tech to promote when actually also underlying that narrative is sometimes this suggestion that AI is going to be so, it's going to be so useful, it's going to solve all our problems that the best thing we can do is just crack on with it and then it'll figure out how to pop up the damage afterwards and of course the climate doesn't work like that, you know, like the damage will be done and the climate and and in particular, you know, are living, are living world ecosystems don't work like that, you can't repair the damage necessarily, you've got to stop the damage happening in the first place. So, you know, it's a very polarized narrative that is coming out of essentially out of Silicon Valley and has been, you know, has been repeated, I think, by a lot of, by the big clasenitech industry and by kind of those people championing that narrative in government, but I do think there is scope for kind of other types of story within this. I think we, we need a story about AI that literally brings it back down to earth, so I'm very interested in how the AI systems, the technology systems that we use, you know, more generally have kind of physical materiality, they are, you know, they are warehouses and cables and metals and, you know, and land and water and all these things and they are also people who make, they're also people who make these things people, use these things people are impacted by these things. So, I think first of all that story needs to kind of bring it back down from the cloud if you like and ground it in our physical reality. And then I think actually we need to, we need to grow a story about a different kind of relationship with AI, one in which we figure out how we as humans can work in kind of productive and youthful collaboration with AI, but in a way that aligns these technologies with our, you know, in a way that is values led and aligns these technologies with the world that we are, with the world that we're trying to grow and kind of and continually ask questions about whether they are serving that purpose or not. And I think that that narrative is, that narrative that is less polarised and that has curiosity at its heart is, is for the most part really lacking. It's such a challenge, these two sides that you talk about and I feel like you've really articulated well how so many charities and tech-for-good organisations are feeling. There is this massive fear that if we don't just crack on, we are going to be left behind as a sector. We've got limited resources and the thought that AI can actually speed up your processes and, you know, really be able to expand your impact is really, really exciting, whilst at the same time so many organisations are really concerned about these risks and the environmental impact, but they don't really have the resource or time or, you know, I mean it's a huge topic to explore and they're, you know, they are concerned about these technologies doing more harm than good. So where do you think that balance lies between this experimentation, making sure that we're not falling behind, but also this caution for these organisations? It's such a dilemma, isn't it? And I'm always reminded of what the technologist Rachel called the cut 'Has to say' about this, where she talks about, you know, I'm always worth remembering that kind of phomo fear missing out is not a strategy and I think sometimes we feel like, you know, very driven by this fear of missing out and getting left behind and, you know, one of the questions that I always find most of us being as kind of what, yeah, but left behind on the way to where, you know, so whenever we're thinking about these things, we need to think about, well, do we want to repeat this narrative of, you know, our development being kind of a race to somewhere, you know, do they have to be winners and losers? Yeah, what does missing out actually look like? So first of all, I think we kind of need to question question that a little bit, you know, and question the values and the narrative they're underlying that because because actually it really, again, it really suits the narrative, the big tech companies to position this as a kind of, you know, you've got to get on board otherwise you're going to get you're going to get left out and I think I really, I'm really sympathetic to and I really understand where that anxiety and where that narrative comes from, but I think also we need to be kind of mindful about what we're choosing to adopt and what we might, what we might also be losing it on or leaving behind, you know, in a rush. And the second thing I would say about this and I think it's, this is a really important point from my perspective is I, I have a lot of empathy for kind of small organizations, you know, small SMEs, small charities, you know, we've got a small team of people really committed to a to a cause really committed to making change in their communities or whatever they're chosen causes and tearing their hair out over whether they should be, you know, how they should be approaching these kind of these technologies and I really feel like actually the kind of the tech sector has done this kind of incredible job of pushing all this sort of guilt or responsibility onto individual consumer choice. So people are trying to figure out, you know, how many liters of water in my tech GPT search, what does, what does mindful use look for me? How can I make sure that my technology that this uses is kind of sustainable as it possibly could be? And while these are all worthy questions, actually, I think there's a really fundamental design problem. So we shouldn't be having to make these choices as organizations are three or four people, we should be able to be choosing technologies with confidence that are effectively labeled for their energy, efficiency that are transparent about their water use where we can make informed decisions based on actually design choices that should be being made higher up the kind of the pipe. And I think we see this with lots of technology that actually where, you know, there's been, you know, in fossil fuel industry has been a kind of master's of this, pushing it all onto individuals in the carbon footprint without actually placing me, without placing the emphasis on the kind of systems change that needs to happen and then kind of making people feel guilty about what they're doing or whatever. And you know, this is a classic systems change problem. We've got, we have got a system, a technology system, a data system that is absolutely designed in its current form for funneling and channeling profits into the hands of a tiny, tiny number of people, essentially, you know, a tiny number of tech billionaires. Whilst at the same time making, you know, people who are, I don't know, trying to run a community art project in South Wales or whatever it might be, actually kind of their consciousness is a rags by, you know, should I be using this technology, should I not be using this technology and the power imbalance there is all the wrong way around. And I think we really need to be placing a lot more emphasis on, on big tech to clean up its act rather than kind of actually feeling, feeling too guilty about what is probably quite modest use at the level of organisations and individuals. Now, we can come onto this and it doesn't mean I don't think there are things that people can do. I think there are lots of things that people can do. But I also think that it's really important to remember where the responsibility lies. We're placing too much the burden of responsibility on those individual users and, you know, inevitably those of us who work in the not-for-profit sector broadly or the kind of, you know, the social impact sector are likely to feel that tension even more acutely because actually we're led by, you know, all our workers about our values, we're led by our values, so we worry about these kinds of things. And that actually has a disproportionate effect, I think, on people in the sector and prevents people from making technology choices that actually might increase their impact. So I think we need to, we really do need to think about it as a systemic issue. I would then, you know, you talked about the balances between kind of experimentation and caution. You know, there will be a different answer to this question for every organisation, depending on your risk appetite and what your risks are. And I think what's needed then is a, you know, within each organisation, a kind of clear plain English policy about what your approach is, what kind of risks you're comfortable with them, and what kind of risks you feel less comfortable with. And that is very much led by your values. So, you know, if your values are all about, let's say your values are all about, you know, respecting the input of people's, you know, respecting people's creativity, value in people for their work, then you might want to use however policy, which is like, actually, we're not going to do any image generation, for example, because we know that that is disposing people's jobs and what, I mean, that's just an example, something you might choose to do. Those values led, but there might be other areas where you're, where you're quite happy to experiment, where perhaps other organisations might, might be more cautious. I do also think that experimentation is really important. So, one of the principles, the first principle that we talk about in our report is the principle of curiosity, that actually, because when we, when we are curious about these systems, we start to understand what their limits are, what their potential is. And actually, I think makes this better place to engage with kind of building, advocating for and building better systems, if we kind of know how they have a bit of a sense of how they work, what they can do for us, and kind of do for us, and kind of unlock the, unlock the black box a bit. I do think we need to, you know, and I know this won't be everybody's view. I know there are, you know, definitely other people who are more in the kind of, actually, you know, we need to think about modes of resistance and decomputing, or whatever, but I think for, I think for most people, there's kind of mindful, curious experimentation, so you can really understand, understand the opportunities in a kind of controlled way, without kind of throwing everything at it too quickly, but exploring in a managed way in discrete use cases, what the benefits might be, is a really good way to proceed and kind of learning through kind of testing, iterating, reflecting, making sure you have that reflexive cycle. It's probably the right way to proceed. Wow, there's just so much, you just got so much amazing information, that I think is just so relevant to charities, third set of organisations, tech for good focused organisations, and I really like that sort of emphasis, and it comes out in the report as well around, you know, where that responsibility lies, and we will talk a little bit more about sort of advocacy and some of the things that perhaps we can collectively do to address that issue more. You did start already mentioning some practical things that, you know, organisations can do to use AI responsibly. Can you tell us a little bit more about some of those things? It's really important to start with the organizational values and think about how you translate those into technology use. At Friends of the Earth, just to give some examples, we've got some, in addition to the principles that are kind of, you know, a report, when it comes to our kind of internal adoption, we do have some specific principles that we're thinking about. The absolutely critical one that we're trying to adopt is this idea we're calling it just enough AI, comes from the idea of kind of just enough internet, which is also an idea that has been kind of pioneered and championed by Rachel Coolvercut. In other places, some people talk about digital sufficiency, for example, but, you know, actually, what can we do to favour? Are there ways in which we can kind of favour the, the smallest application of AI to kind of meet the needs? We're not saying don't do it, but we're saying actually just think about what does, you know, what does just enough to get us to the place we need to get to, you look like, you know, and so some of the questions we ask ourselves are things like, okay, well, if the AI route perhaps cuts or sort of reduces the sort of manual effort for a task by about 75% roughly, or at least that, and, you know, significantly reduces the, you know, the kind of compute energy. I mean, we're kind of user-estimating for that, but significantly reduces the compute energy, then it might well be worth doing, actually. So, yeah, so some things to think about, it's kind of just enough AI approach. Again, this would be one that's very common to a lot of people, but they're kind of the human and the loop approach. So, actually, when you're thinking about response, we're used making sure that if there is a kind of, if there's a high-risk decision point, if there's something public facing, then you are, then, you know, there is a, there is a human always involved in reviewing and, you know, assessing any outputs. A really important one for us is that we will never use AI for, we'll never use AI for any sort of automated, for recruitment, for any sort of automated, you know, certainly not for any kind of automated recruitment decisions, and, and we also, we will not be using AI for anything like kind of productivity surveillance or anything. I mean, we, you might be able to take this into granted, but other people might be starting to do that, and it might be starting to creep in. So, thinking about, okay, well, what, you know, what are your red lines? What is really important to you? We have a really wonderful kind of creative team who produce amazing kind of graphics and whatever, you know, again, it's really important that our voices and authentic voice, because actually our voice is about empowering local grassroots communities to take action, and it's really important people know that when they are seeing images from us, they are, you know, they are not generated there, AI or anything like that. So we have some, you know, we have some. we absolutely have some red lines about that usage as well for all sorts of reasons, both protecting creatives and actually protecting the value and the authenticity of our work, of our voice. And there are other things that people might think about like open disclosure. So actually when AI has been used in a few views, AI to write a report or write an article or something, actually you can explain, well, actually, for example, AI was used in the early stage information gathering to look into sources or whatever, say what you do, be explicit about it so that you're building trust and you're retaining the trust of your audience. I think that's a really important kind of practice. Some of those steps are really important, kind of practical, mindful uses. There's also, I think, things that people can do that speak more to the advocacy side. So actually, like as all organisations, if you are procuring services, if you're procuring technology services, then you have a lot of power actually because you can ask the providers of those services, well, what do you know about where your software is hosted? What do you know about the carbon footprint, the energy footprint, the water, whatever, the servers where your software is hosted? You can ask those questions in procurement. There's a question you can ask around things when you're not just in AI services, those are also questions you can ask. I'm sure you've talked about this a lot before. Those are also questions you can ask around things like web design, for example, how are you minimising the impact of your web design and hosting? People won't always have answers to those questions. Sometimes they won't have answers to those questions because there is a gap around reporting and actually we know very little about water usage and so on. But actually, there's a power and just asking the questions. And part of RISM once we're used is I think comes down to actually asking, being conscious of what you can influence where you might be able to exercise and influence and using that when you can in things like procurement. Yeah, and I think it's so interesting listening to, listening to, I suppose, how many different elements that are to this whole conversation because I really feel like since AI actually, it's brought into our focus a little bit more. This idea about having a digital footprint was actually every time we send an email, we store something in the cloud, we have a digital footprint, but it seems like with AI, it's highlighted the issue to us much more because of the massive amount of data and cooling the data centers and all of this sort of extra cost on the environment. But actually, it's making us perhaps thinking a little bit more about our impact on the whole in terms of our digital footprint. And I know you were talking to me last time we spoke around having that. I think you called it like a "data hygiene day" or "digital hygiene day" where you get together with your team and you just delete loads of different things. Do you need to store these? I'm interesting after our conversation. Some of my teams started looking into, and this wasn't due to this, it was just sort of by coincidence. They started looking into the fact that some of the way that our systems are set up, there doesn't actually delete emails when you delete them, it still sort of stores them in a deleted inbox and they contacted the IT department to say, "Can we please make sure these are fully deleted after 30 days?" And even though that seems like a small thing, it's actually like, again, making maybe us all thinking a bit more around what are our policies in terms of this. That's a great example. And yes, I think I called it "digital cleanup day" or some people kind of call it digital decluttering. And there is an annual international day in March but maybe it's a good thing to start working towards. Which encourage people to kind of do that digital cleanup. We are storing a kind of increasing mountains of data and of course the AI relies on these mountains of data for training purposes and whatever, but I think we've become quite casual about storing not deleting photos, videos, all sorts of files that have all sorts of very large files that we don't necessarily need to keep and we end up just storing them for ages because we don't have necessarily have good practice around that. And actually, if everybody took a bit of responsibility for doing that, whilst obviously being really clear about the records that we need to keep, whatever, then I think that could make a big difference. I can't put a number on it, but I feel like it's something that we could see more people adopting. And in the days when an archiving and storage was actually physical, the key practice of archiving was figuring out what is it, not what to keep, but what to get rid of. Archiving is a practice of thinking about, okay, well, we don't need this anymore, we figure we can forget this. And that's as important as thinking about, what is it that you're going to keep? So I do think those kinds of things can make a huge difference. And there's lots of, there's so much, I loved your example of ways in which you can automate the systems, like the email system. There were loads of examples like this as well that I, you know, a really small one, but I've realized recently is if you ever, if you take a transcript from some video-compensing services, so things like, you know, I sometimes take a, I sometimes take a team's transcript on Microsoft Teams. And the size of those files is enormous. And what I've realized now is if I like, if I save it as a text file, because that's all I need, it's the only, is it only need the text, not all the kind of hidden metadata and whatever within it, it reduces, reduces the size of the file by about 99%. You know, it's, it's, yeah, so there's lots of redundancy and lots of obsolescence built into our systems, and actually, you know, and if you are a small organization, if you're a small charity or actually probably all this storage is costing you quite a lot of money as well, potentially. So there's a, you know, there's a financial benefit to just sitting down and having a bit of a clear route, you know, every self as well. You might actually find that it, yeah, it has a, you know, it has a cost-saving as well as, you know, as well as an environmental saving. Yeah, it's so helpful. And I think those practical things are really useful, because so many organizations they want to be able to have something practical they can start doing, so that's something really, really helpful. And it would be really good, though, to hear about some positive use cases for AI. So either within Friends of the Earth or other environmental initiatives that you've come across, could you share some of the positive or potentially positive use cases? Yeah, I think some of the most interesting positive use cases for AI are probably the ones that people have been using for quite a while and didn't, didn't necessarily think about as AI, because it's only when we've started people starting using, you know, generative AI that somehow, this has become a bit more in people's consciousness, but one of my favourites I've been using for a long time are some of the kind of nature identification apps, so things like Merlin for identifying bird songs, that's not my big one, the people know, or I naturalist for which is a, which is an app that basically uses users' photos to help you identify mostly plant species. You know, those things are, and those things are incredibly valuable, they're valuable, they're basically using kind of AI pan matching. I've learnt so much about the wildlife around me, you know, I've heard, I've been able to identify rare birds in my neighbourhood that I didn't know were there, and then I can't see just because I've heard a call and thought, what was that? You know, I think that's incredible, that's that opens the up an incredible window on the world. And then in terms of actually practical kind of organisational uses, I'm sure a lot of listeners will be very familiar with with Tefagood Southwest, kind of living lab pilots, but I think for example what the Centre for Sustainable Energy are doing in process, I think a chatbot that might help them with their retrofit service, so they can actually focus, they can focus their advice on energy efficiency, energy saving on either kind of cases that are a bit more complicated, working more with vulnerable people, you know, but actually really, really prioritising those cases where, you know, that where they really need that kind of human input, but actually that means also they can, they can do more with the resources they've got, I think that's a, you know, again, that's a, that's a great example. Within, within Friends of the Earth, one of the ways we've been trying to think about AI, is thinking about, okay, well where, where might we be experiencing bottlenecks in terms of, you know, limits on what we can do, and are there ways in which AI can help address those bottlenecks, particularly if they are, you know, particularly if they're sort of, you know, desk-based bottlenecks. So it does take examples from, you know, my own kind of innovation team and from our sort of experimental practice, because I think it's better to speak to kind of, you know, the experience of, me and my immediate colleagues rather than kind of more widely, things like, you know, things that we would never have been able to do before, but actually can do very quickly to bring an idea to life. Like, we do occasionally use image generation for storyboarding, for example. Now we'd never use that with an external audience, but actually for a trying to show, okay, here's like a, you know, here's a concept that we want to bring to life, and, you know, and it's not something we would previously have used a graphic diviner for or something, you know, it's something where probably I'd have had to spend half an afternoon with my pretty patchy sketching skills, try to draw something out, try and make something come to life, you know, that's not necessarily my strongest suit. And actually now I can, you know, generate a storyboard that I can put in front of someone that can be like, look, this is like, this is what the user experience of this would be like if we, if we went done this approach. Now, what do you think of? bit, what do you make of it? So that's an example of a bottleneck where we can move through to prototyping much more quickly in quite an exciting way. And there are some parallels as well around unblocking a bottleneck around researching new topics, for example. So we have been using complexity a little bit for researching new topics, understanding a field who's already working in this area. What are they doing? What are good examples? Are there examples of practice from elsewhere in the world? Now, obviously, we could do that using more traditional Google searches and literature reviews and whatever, but the time saving is massive. And we'd never then take that face value. We'd always then go away and check out the links, make sure the links really are what they say they are. And again, it's for internal purposes only, but something that might have taken us a week, we can outlaw together in an afternoon, for example. So I think in terms of there are productivity benefits in some areas when you use mine fully. But I do think we need to approach all of these of the course and I come back to the kind of organizational values around really being confident about the facts. There are examples that are kind of valuable and tunnel usage and there are examples, I think, a couple that I've just shared, where AI is really being supporting this delivery of public racing projects like with the Centre for Sustainable Centre for Sustainable Energy. And I suppose a final case, which is probably not one that's so relevant to small organisations, but one of the things I also love as an application is I think it said AI is brilliant at kind of patterns, but there are some fantastic examples of people using AI to do things like identify, identify areas where they think illegal deforestation might be taking place, illegal waste dumps, those kind of things again, they almost always need kind of checking out and ground-tree thing that might be something, you know, it might not be quite what it looks like. That's incredibly powerful and actually that the potential AI use in those use cases for actually delivering a kind of cleaner, healthy environment of focusing enforcement resources where they're really needed, I think, is really exciting. Yeah, I think it was really important to talk about these like exciting potential developments as well. I think that's why it's also so important that charities, third sector, socially-focused organisations actually do this work because actually we are the organisations that are going to hopefully be able to explore how this can be used for good as a technology. And really this takes me on to the point, and you've mentioned it a few times around advocacy. So at TETA for Good Southwest, we have a network of organisations passionate about using technology for positive impact. And you have talked about how the responsibility to use AI ethically and sustainably is often pushed onto these individual organisations, but really we should be pushing back on those people with power. You have spoken already about things that we can do, like start asking in terms of procurement. But are there other things that we can do? Like what role do you think networks like ours can play in this collective advocacy piece around AI in environmental justice? Yeah, thank you for that question. And it's one that at Friends the Earth, we are still a little bit trying to work through and talking to those other organisations who we think are doing really important work in this area. I'm in a toilet at the work of an organisation like Global Action Plan. Look them up if you don't know them. Global Action Plan have been working for a long time on the interface between big tech and the climate crisis. They're currently leading a legal challenge with a campaign organisation called Fox Glove about a hyperscale data centre in Buckinghamshire, but mainly about the local authorities' failure to conduct a proper environmental impact assessment. And trying to figure out what we can all do is networks and so on in this space is part of what we're thinking about at the moment, what are the different roles that people can play? I would say that beyond some of those questions around kind of procurement, I think there are also questions that we can be asking of our local authorities, of our local planning authorities. And that's why it's interesting that you've got a network like kind of Teyfor Good South West that is actually play space. So what do we know about what is the infrastructure that's planned in the South West? What do we know about that? What do we know about what's coming through? Are there like to be any AI growth zones? What have they said about? What do we know? What do the local water companies think about this? If they've got a view, where's the energy actually coming from? So first of all, trying to understand, build a collective picture of what is the situation here, I think is really useful. And actually then starting to ask, starting to ask some of those questions of whether that's of your local, depending on the issue whether that's of your local council or that might actually be of the regional mayor because the regional mayor has her responsibility for regional mayor to have particular responsibility for economic development and so on. After you're them, how are they ensuring that development that is coming through is still going to be in line with climate targets, carbon budgets and so on. And importantly, we have ambitious goals around 30% of managing 30% of our land for nature by 2030. So how are we making sure that data centres and data centres in this infrastructure is contributing towards those goals? Sometimes data centres aren't that – it's not always that easy to spot them in planning applications. So one thing that we've observed is that planning applications. When you see applications, the data centres coming through often, they are initially labeled as under a particular category, which is called storage and distribution. So you can't necessarily tell initially what's the warehouse and what's a, you know, what's the warehouse and what's the data centre in a planning application. So actually, there's a question – I think we can ask questions of, you know, you can ask questions of your local council as a particular place on the planning committee is, you know, what sort of applications are coming through and how are you assessing them and what information you're asking for. I think that, you know, that's kind of if you like a role that individual members or citizens can play. I also think that actually it's really, really important to keep these questions on the table and really live and that's part of our advocacy role too. So a couple of weeks ago, it was Bristol Tech Festival and, you know, there's lots of hype and lots of excitement about all sorts of exciting, you know, innovations, including lots of AI-based innovations. And I actually feel that even compared to last year, there was a little bit less about kind of the environmental kind of impact or the social impact of some of these technologies as well. I mean, you know, I saw some, I went to a showcase and I saw some technologies that to my mind were pretty alarming around kind of facial recognition technology, for example, which, you know, without the right, without the right safeguard, it's very, very quickly, it becomes a surveillance technology. And, you know, in a surveillance technology that is directed at those, you know, these are often directed at the most marginalized groups in our society as well. So, you know, we really need to kind of keep asking these questions about actually what is this for? What are the trade-offs? You know, are the benefits worth the cost? And actually just keep being that voice, you know, something in network like Tech for Goods, can cannot keep higher this kind of this narrative space that is somewhere in between, you know, that, yeah, but it has kind of a positive vision for how these things can be used, but also brings a conscience into these spaces, I think, because we are all values led, and we can bring those values in, and I think we shouldn't be, we shouldn't be shy of asking those questions, and we shouldn't somehow feel like, you know, that it's kind of a stupid question if we're asking it, you know, if we don't, if we don't know the answers around environmental impact, or you feel that people aren't being clear about them, then we should have the confidence in those spaces to stand up and ask those questions. Thank you so much. And such important valuable questions for us to keep in mind and to keep asking, it is the Tech for Goods Southwest podcast, so we always ask, what does Tech for Good mean to you? So for me, Tech for Goods means that technology, that, what it means, I think it means harnessing the liberatory potential of technology, right? So how can we, how can we, it's technology that enables us to be more fully human, to be more fully alive in the world, to experience the world, the living world around this more, more fully, and to enable everyone to kind of develop their potential, and liberatory technology will mean different things for different people, you know, for some people it will be technology that enables them to kind of overcome, to overcome barriers that they might have to engagement, you know, with, with all sorts of, you know, with the world in all sorts of ways. And so then for some people it will just be actually technology that enables them to step more fully into their human rights. humanity, but I think that's what tech for good means to me. It means unlocking that kind of liberatory potential. Wow. It's such an amazing answer. And like, it's been great talking to you. You have so much knowledge and experience to share, and it's so relevant. I think I said when we first met, it was quite exciting because I work with lots of charities and third-set organizations, and we've already been pointing them to your report, which is just really excellent, because I think that we are trying to all work things out and having that expertise that you can share and that you're sharing so free now is just really, really helpful. Yeah, so just massively grateful. I know this is going to be really useful to so many organizations. Finally then, where should organizations look for guidance if they want to be responsible in the space or just carrying on following this work, you're doing how do we learn more? Yeah, I mean, obviously, one of the first places to look is our report, and we're going to be publishing some new resources quite soon as well, probably a policy position piece on data centres from friends of the earth, you know, that'll be a national piece rather than just kind of south-west space, but hopefully that will provide some more specifics on some of these questions in relation to the UK. I mean, I often direct people towards, and I'm sure you have in the past as well towards the resources available through CAST. They've got a really good AI resources kind of page and their digital leads network is also producing really good resources in AI, so I think that's a good place to go. We're also, founder members of something called the Innovation for Impact Network, which is actually a network primarily of international NGOs that includes people like WWF, Amnesty International, you know, internationally, and we've produced some additional resources on kind of AI in the NGO and kind of charity sector, so that's another place to go and look. And I always, you know, I can't recommend strongly enough that what's available through the Green Web Foundation, I think they do amazing work and have some amazing resources, including a guide to that explores a little bit more how you can dive into these procurement questions, you know, what the questions you can really ask. And, you know, and finally, I would say, you know, we signed, we were signatories on a civil society statement back in February, developed by something called the Green's Green Coalition, who again have loads of amazing resources work quite a lot with the Green Web Foundation. And they produce this, you know, they, we as a coalition producer statement called Within Bounds about AI within planetary boundaries. And I think that that is still for me the clear start to accumulation of what is the kind of technology that we need and that we should be advocating for. Amazing. Thank you so much for sharing all of this and thank you for talking to me today. It's been absolutely amazing to have this conversation. Thank you for having me. [Music]