Using Generative AI in case recording

[Intro] 

Sarah: Welcome to the Research in Practice podcast for the Children's Information Project. In today's podcast, we discuss the use of Generative AI in case recording in social work. Generative AI refers to artificial intelligence systems that can create new content such as text, images or audio. It does this by learning patterns from existing data. These models can generate outputs that resemble human creativity and expression, offering innovative tools for a wide range of tasks. The emerging use of Generative AI, or GenAI, is changing the way social workers produce case recordings. For example, social workers are using GenAI to produce case notes and meeting minutes from voice recordings, or to analyse and synthesise information. Using GenAI in case recordings has many potential benefits and offers an opportunity to reduce administrative burden for social workers. However, it also comes with risks and ethical challenges. 

On 17 July 2025, Research in Practice hosted an open invitation webinar about using Generative AI in case recording. There was an overwhelming response to the webinar, which indicates the level of interest in AI. The webinar concluded with a panel discussion that explored a range of things. This podcast captures the panel discussion. Sophie Woodhead from the National Children's Bureau facilitated a discussion between Mark Peterson, the Head of Data and Insight from North Yorkshire, Mairi-Anne MacDonald, the Deputy Director of Development and Innovation at Research in Practice, and Sarah Rothera, a Research in Practice Associate. The panel responded to a range of questions and comments from participants, including themes around the lawful, ethical and responsible use of AI, the influence of Generative AI on voice and children's information, the impact of AI on critical thinking and professional development, and we also explored the importance of AI literacy, and roles and responsibilities in AI implementation. Now, let's tune in as Sophie begins the panel discussion.

Sophie: We'll, kind of, go round the room of panellists, and ask some specific questions, but we'll be focusing on roles and responsibilities, voice and AI literacy. I've noticed in the chat that there have been a lot of questions relating to the use of this information in a court setting, so we will attempt to respond to that in the course of some of these responses, and we'll be pulling some direct questions as well, from the chat. But I'll start off generally, and also, perhaps invite Mairi-Anne to come in an introduce yourself, and perhaps share some perspectives on what's the driver here, for integrating AI into social work, and specifically case recording.

[Integrating AI into case recording] 

Mairi-Anne: Thanks very much Sophie, and lovely to be here today, and to hear from everybody in the chat. I think we can tell by the number of people who have come along today that it's a topic that's really interesting to everybody, and we all want to find out more about it, and that's part of the problem. Part of the problem is that there's so much that AI can offer, that we all want to do it, and we all want to do it now, and so sometimes, that means that we don't necessarily make considered decisions about how, when and why we should use AI. And just really reflecting on the use of AI in terms of voice, it's really important to understand the dual nature of a case record. So, part of it is around that organisation responsibility, and your professional responsibility, to accurately record your interaction with a family, but the other really important purpose of the case record is to provide a record to the child of their experience in care, or while having worked with a social worker, or someone in the social care team. And if we think about it from that perspective, we need to be really careful about how we are making sure that the child's voice is authentically recorded and used, so that the child actually can then find their record. Now, you can argue that, 'Ok, well, you know, when someone writes a case record, they're actually also translating what somebody says into a paper or electronic copy,' and that's true. But a worker doing that has got an ethical framework for thinking about that, and so, we need to think around, is there an ethical framework in place for decision making around the use of AI in case recording, and how is that applied? And at the moment, each local authority does that on their own. They have their own policies in place, and they apply their own policy with differing amounts of requirement for consent, for how they use that, and for the transparency around it.

So, there is something, I think, about how we have something that is much more transparent, much more rational, and that actually, rather than just rushing to use all of our own things, we actually have some underpinning decision making, ethical decision making framework, that really supports that across all the local authorities, and the use of AI more generally. I think that one of the important issues that's been raised in the chat is actually, how do we let people know that AI is being used? Do we have an ethical responsibility to do that? I would say yes, and how do we do that, and do we do that with any consistency? Do social workers, or social care workers, know enough about the use of AI in their systems to be able to confidently explain that to people, and if we have AI built into our system, and somebody says, 'No, I don't give my permission for my data to be used in that way,' do we have any recourse to actually say, 'Ok then, that's fine, we'll agree with that, and we'll not use that for your data,' and I expect that we don't. I think once we start using AI in a system, it's used across the whole system and it's difficult to, kind of, walk back from that at all. So, that ethical framework is really important, and the ethical framework means that it provides a basis for decision making, rather than thinking about, 'Well, is Copilot better than Magic Notes?' Because relying on, or thinking about, the software itself, is not going to be helpful. It's changing so quickly that making recommendations about specific software isn't going to help your organisation in six months' time. Thinking around how we make decisions, and what we need to take into account when we make decisions about the use of AI, provides a much stronger framework, that's really going to support that.

And I think, you know, we've talked a little bit about user voice there, and the other important voice in here is practitioner voice, and just balancing both of these is important. Generally speaking, the use of AI is argued to be helpful to the worker, because it can increase time to be used with direct work with children, it can decrease time to be used on admin tasks. What we don't also have, though, is what are the benefits for children and families, so how do we understand that? And I think that relying on generic systems that you buy off the shelf actually is not particularly safe, and we need to be thinking far more around how we can use bespoke systems, and generate bespoke systems and requirements, so that we can make sure that we've got an ethical use of AI in practice across the whole of social care. And if we're thinking about practitioner voice as well, we also need to think around, how are we preparing the workforce for the future? What are the new skills that people need? What is the new knowledge that people need to be confident and competent in terms of both understanding how AI is used in the system, and what difference it's making to the data that's being put into the system, but also how they explain that to people in a way that they can make confident decisions about whether their data can be used by AI. And I think we also need to recognise that AI does change things, and so, you know, just to use another example, how many of you can do mental calculations without a calculator? I would probably say not many, whereas my parents can do quite complex mental calculations in their heads, because that's the way they were brought up. So, there's something around the familiarity and the ongoing use of AI that might affect some of the skills that we currently use in social care practice. One of these is critical thinking, and so, there is some evidence coming out of the research and, you know, as Sarah said earlier, all of this is under researched, and we need to do a lot more to find out what we need to understand in the future.

But some of the research is suggesting that critical thinking skills are reduced, because AI means that there's less opportunity to practice critical thinking, because AI is doing some of that thinking for you. And so, we need to understand that, particularly in terms of social work practice, where critical thinking is such an important part of assessment and professional practice. So, if we are using critical thinking less, how does that then impact on people who are new in career, new into the profession, and how does it impact on people who are more experienced, but using it less? So, what are the other opportunities we can provide to practice critical thinking and the other essential skills, if we're not going to be doing that through the use of case recording. However, the point that Mark made, the point that Sarah made, and that is reflected in the chat as well, is that even if we are using AI in aspects of social work practice, that does not take away your professional responsibility. If you're a registered social worker, you've got professional and ethical responsibility to be responsible for the work that you produce. And even if AI has been part of that, and you could argue that the minute you look something up on Google, AI is part of that. So, if AI is part of that, that does not negate your professional responsibility. So, if you're going into court with a report where AI has been used to generate some of it, you are responsible for that report. You can't say it was AI that did it, and there was a mistake in it, you're responsible for checking and having oversight, etcetera.

So, there's something also around thinking about how work will change. So, while we will retain our, kind of, key professional tasks, some of the details of how we undertake these tasks might change, but making these changes in a considered way, that considers both the ethics, the professional responsibility, and our responsibility primarily to the children and families that we're working with, about making sure that their voice is authentically recorded, and is a clear and honest representation of their voice, is really important. And I'm going to stop there, but I can come back to any specific questions, because I've talked for quite a bit there.

Sophie: Thank you, Mairi-Anne. Yes, that was really interesting to specifically, kind of, consider this in the context of the benefits for children and families. But also, to be touching on those points that are clearly at the top of people's minds around critical thinking, around practice, around professional responsibility, and linking that, and having the interface with an ethical framework to really support that but never losing sight of that professional responsibility. At the moment, what we're thinking and seeing is around how those tasks might be implemented or changed, but that level of professional responsibility remains the same. And I'll just pull in a question that's linked before I go to the other panellists, from Jessica Garner in the chat, who's thinking about the role of social work education with these changes in mind, and is there any advice or any thinking around embedding the use of the ethical frameworks into curriculum, to adequately prepare social work students for practice?

[How might AI change social work education?] 

Mairi-Anne: Well, what an interesting question, I'm glad you asked that, Jessica. So, Sarah and I have recently been working with Social Work England to have a think about how the use of AI might impact on social work education, and while we can't share much in the way of findings from that at the moment, that report is expected to be published later this month. And so, look out for that, but I think in general terms, there are people who are using AI in social work education at the moment, and it's a bit like practice, it is happening in some places and not in others. And so, institutions who are more interested in it, and who are thinking about it more, are tending to use it more, and to be also thinking around, what do they need to add into the curriculum for students about the use of AI. But one of the complications that is coming out of that is that, because it's happening in different ways in different places, it can be quite difficult for students to know what to do, for when they go on placement in particular.

Sophie: Thank you so much, Mairi-Anne, that's fantastic, and there is a point on critical thinking, I think, that's going to come up, and I'll come back to it, actually, because I'd like to invite Mark in now, at this point, to think about the overall purpose and driver, but also in terms of roles and responsibilities. What encourages, from the experience that you've had in North Yorkshire, the responsible decision making, and also leadership in the use of Generative AI for case recording?

[What encourages responsible decision making?] 

Mark: I think on the first part of that, there's an obvious thing from a local authority, we were always asked to do more with less. So, it's how can we use the things that we have, the things that are coming along, to try and allow us to try and bridge some of that gap. We know that demand increases, and the resource does not align to demand, so there is that challenge. We know that the data that we've got is probably locked away, in a way that doesn't allow us to use it in the most effective way. AI potentially creates an opportunity, as we have shown with the knowledge mining. And the other part of it is that some of this is done to us. So, suppliers are going to embed AI within their solutions. So, whilst the examples that I've shown are things that North Yorkshire have actively gone out and decided to do, other things just happen. So, if you think about your streaming services, the fact that it's doing AI-generated recommendations of content to watch next in the background, you probably didn't read the end user license agreement when they changed it and they sent the notification, but they just put it into the platform, it just happens now. So, there is a changing landscape that we can either bury our heads in the sand, or we have to engage with, and understand, and think about how we approach that. Interestingly, DSIT was mentioned earlier, Department of Science, Innovation and Technology, and they are currently advertising for board members for a panel around AI ethics. So, central gov are looking to get people from different sectors involved in that conversation. There is an all-party parliamentary group around AI and how we use that, and what's the right approach as ethical considerations.

So, there is lots of stuff in their space, and even, actually, Meta, who are the big, obviously, evil, dominating world leaders, they run a programme called Open Loop, and Open Loop is looking at how we can ethically govern and be transparent around things like foundational models. So, they're the model that sits right at the very bottom, that all of the other things are built up on top of. So, there's a really important bit in there, that actually, ethics is at the forefront of this, and thinking about how we do this, but being aware that it's coming, and in a lot of places, it's already here. So, I think that's, kind of, the backdrop. In terms of the roles and responsibilities, it's an extension of that. I think leadership understanding that concept, that the technology is here and we have to leverage it appropriately, but there is value. That's different levels of value in different places, and different levels of value for different people, but it's an awareness that there are tools, and I do think of them as tools, to be able to solve potentially part of the problem. So, if you start with the outcome rather than thinking about the technology, the whole solution to that outcome is unlikely to be AI. There will be lots of different components, and AI might be one of those. I think the other thing that we've probably slightly done as a disservice to ourselves, because the buzz phrase that gets used is, 'Human in the loop,' and, 'Human in the loop,' makes it feel like we're just part of the machine, and we're not just part of the machine. So, one of the phrases that we've tried to use at North Yorkshire is, 'Human in the lead,' we've changed one word, but it massively changes the context of the statement, which is, 'You're in charge, you're accountable for this, you're responsible for this, this is on you, and you can use this, and it is valuable in certain places, but this does not replace you. You absolutely have to own this, and use it where it feels right.'

Sophie: Thank you so much, Mark, and I think that, 'Human in the lead,' is certainly a much better framing. An interesting question around, kind of, linked to roles and responsibilities, Mark, and the changing landscape that you referred to - we're all recognising that this is a very dynamic and changing space. So, if we still need research in certain areas, why would we proceed towards implementation?

Mark: It's exactly why we started with an AI ethics impact assessment, because we had to start with, 'Is this the right thing to do?' And in some cases, it isn't. There was a large tech company that created a model that was designed to analyse CVs, and they trained it on historical information, and it was a tech company, and surprise surprise, historically, tech companies were slanted towards male employees, so it started selecting all of the male employees. Absolutely not acceptable. So, there are things in there, that just because you can do something, it doesn't mean that you should do it. The important bit comes back to a point I made previously during the presentation, I think it comes down to risk and value. We have to accept that it is not 100 percent right, but people are also not 100 percent right, and if it can provide value that is greater than its risk, then it's something that you might start to consider. It doesn't mean you definitely need to say yes, it's not, kind of, 'It gets to 51 percent, and then we're definitely doing it,' but it's those types of things where we have to start to look at some of these things. Because they are out there, and actually, if it gave 80 percent of value, or 90 percent of value, even though it's not 100 percent, it's still something we need to have on our radars.

Sophie: Thank you so much, Mark, for that really thoughtful response. Sarah, I'd like to bring you in. There are many more questions in the chat related, kind of, to that point, but also to the point of levels of surveillance, and that linking of information in the advanced way, the linking of information and the adverse effects of that, which we might consider a little bit later. But Sarah, to the point around AI literacy, and again, we've touched on this a bit around professional responsibility, maybe training, but it would be really interesting to hear what considerations or support might professionals need to build in confidence or critical understanding of AI.

[AI literacy] 

Sarah: I think the question of AI literacy is central to everything that we're talking about, because I'm a little bit glass half empty on this. So, when I look at the poll results from earlier, I see that we've got quite a big audience today, and we've got 39 people who are using AI in case recording, but only around half of them feel confident in using it in that way. So, being glass half empty, that tells me that there are a lot of people who do not feel confident with using GenAI, but it's being used, whether that's with their employer's consent or not. So, I think we have to really keep the question of AI literacy central in all of our planning and purchasing, and use of AI, and to me, there are two learning curves. So, when you introduce the product, there's the learning curve of learning how to use the product. AI is actually quite intuitive, so quite often, that learning curve is much lower for people, which I think can give a false sense of confidence, and I've worked with social workers on a whole range of different projects over the years, around data, and that have involved IT [information technology] and different ways of being efficient, and all of those sorts of things. And I've found that, you know, people have, obviously, different levels of understanding and capabilities with technology, but I've found with AI, that's reduced quite significantly, because it is so intuitive, which I think does create that false sense of confidence. And the other part of that learning curve is not just learning how to use the application, but learning how to use it lawfully, ethically and responsibly. And I think that when we're looking at that AI literacy journey, it certainly does start in education and training, because the questions and Mairi-Anne's response have touched on this a little bit already.

So, this is not just around, how do we make sure that social workers are using it, and maintaining those professional skills, but what does that actually mean for social work students? You know, how can we actually refit things so that they are actually getting the education that they need to be able to do this? And I should define AI literacy as well, because it refers to the skills, knowledge and critical thinking needed to understand what artificial intelligence is, and to recognise what it can and can't do. Knowing what it can't do is absolutely central to this, and I know I'm, sort of, reiterating some of my points here, but I just really want to make sure that there are some takeaways here. You know, to give you an example, because I've noticed in the chat, there's been a really strong theme around court, and that's a really good scenario for us to think a little bit more about. Because let's imagine a social worker has used Generative AI, produced a lot of case notes, there are also other forms of AI being involved in the system. When you get to court, the level of accountability that we experience just escalates. You might have a family member who is not feeling good about the intervention that they've had with social care. What does this mean for the trust that they have in the information that is within those case notes? What does it mean for the trust in the reports, and the chronologies, and all of those sorts of things? So, this does have real life implications if we get it wrong. Can the social worker speak to the way that AI has been involved in decision making? Can the social worker speak to the quality and the accuracy of what's written in the reports, bearing in mind that cases transfer from social worker to social worker at times?

So, it does raise real life ethical concerns that we need to be mindful of and, you know, to me, this comes back to, to refocus on your question there, Sophie, what support might professionals need to build that confidence and critical thinking. To me, it comes back to having really good quality assurance procedures in place, so making sure that there's a lot of work invested into what that actually looks like at an organisational and team level, because I think that it is quite contextual. Thinking about what AI literacy looks like within your specific context, because it's going to mean different things for different teams and different people, and also for different applications that you're using. But it's also developing the skills and knowledge, so understanding that if you can craft a prompt, you can craft prompts in all kinds of different ways - some are better, and some are worse. So, helping people to, sort of, develop those skills around prompt engineering, thinking about what actually retrieves a better result, but also thinking about what that verification process looks like, and I think the business case for AI often lands in the space of 'It's going to save us time, it's going to save us money, it's going to mean that we're going to end up spending more time with children and families.' And I'm quite sceptical about those claims, because I think, yes, it does save time, it certainly saves cognitive load, but we've heard already that that could come at a cost. But also, a lot of that time that you save gets redirected into the verification and validation process. So, for example, if you have a generated chronology that is going to into court, every single line of that chronology needs to be verified against what is written in the case files.

So, you start to, sort of, see how, yes, there are time savings, but there are also redirections, and that net time saving has a little bit of a question mark around it, for me. I think it's one thing for a social worker to verify what they've just heard in a visit or a meeting, I think that's a much quicker, much simpler process and time saver. But when you get into those more complex reports and documents, I think the verification process is actually quite time consuming. I sound very pessimistic, I'm actually very optimistic about the transformative potential of AI in social work. I think that it can help us with our work, it can solve a lot of problems, but the way that we use it is absolutely critical, and that comes from AI literacy. If you're very trusting of AI, which I encourage you not to be, then you're not necessarily going to invest as much time in the verification of the outputs, and that's something that came from that research I was talking about with Microsoft and Carnegie Mellon. So, your level of trust in AI is what triggers your critical thinking skills. If you're very trusting of it, it doesn't necessarily trigger them. If you're not very trusting, it will. And I noticed as well, there were some questions in the chat around people's consent, and to me, this is not just a question about AI. This just goes back to normal processes of consent. We don't record people unless they consent, that consent is informed, that consent is freely given, and I think that they're just general principles that we apply to any part of our social work. I just wanted to finish on that.

[Monitoring of information and consent] 

Sophie: That's brilliant Sarah, thank you, and that does talk to that point a couple of people have mentioned in the Q and A [question and answer] around the linking of information, and whether families are aware, and how that information is then going to be linked, joined together, and therefore the, kind of, ongoing effects of that. So, is there anything in the context, whilst, indeed, consent is an issue regardless of AI and, kind of, with AI as well, so is there any other thinking from the panel around that informed process, when we're considering, now, integrating different intelligence to be able to link information? And there was a question there, as I mentioned previously, about that feeling of surveillance and, kind of, much closer monitoring of that information when we're thinking of it in terms of, probably most specifically, geographic monitoring, and others as well, networks too. Any comments first from you, Sarah, and then perhaps over to you Mark, if you have anything to share on that.

Sarah: The question of surveillance, probably, is not so much around Generative AI, but to me, that really comes in when you're thinking about predictive analytics and those sorts of things. I think there's a natural amount of suspicion when it comes to AI. I think we've all been conditioned, you know, by the movies, around not trusting AI and what it can potentially do. But there are legitimate fears in there, I think that there's potential for us to message around AI in a way that is potentially helpful, and potentially harmful. So, we can talk about AI being able to free up parts of our time, we can talk about it with children and families around, you know, this is actually a really helpful tool that can help us to understand really complex problems. But likewise, it can be perceived as something quite scary, not just for children and families, but for social workers as well - because there's a question of surveillance around, well, what does this mean for surveilling social workers in their jobs? Does this mean that we're going to be looking at the way that they're working and spending their time in different ways because we have that analytical capability? So, I think that there are questions of surveillance that we need to be mindful of, within the organisation as well as our work with children and families too.

Sophie: Thank you Sarah, thank you for that, and Mark, any reflections from your side on that point?

Mark: So, I'll try and give the other side of the fence, because I would absolutely reflect all the comments that Sarah's just made. I think there's an interesting thing, that people sometimes have a different perception to what our internal perception is. So, every now and again, we'll get the comment of, 'Well, I just assumed you were doing that with it anyway,' and we almost have this assumption that we are going to do something that somebody didn't like, and actually, when you speak to people, we actually find out that they just assumed that we were doing it, and actually, they wonder why we're not. Because they can see the value in us doing it, and we take the significantly cautious approach in terms of doing things. It's interesting, when we look at people's information, if you think about all of the information that social media harvests from an individual, their mobile phone harvests from an individual, we give away huge quantities of information, which are absolutely monetised, and are not for social good. So, as I say, I would absolutely reflect all of the concerns and caution that Sarah's highlighted, but I think there is also something about trying to understand some of the younger generations, and their perception of the world, and the fact that they've grown up, and that this is literally just part of what their experience is, and how they live. And it's to some degree, possibly scarily so, normalised - that actually, maybe there is a little bit of space where we need to be a little bit more aspirational with some of this. Because there is, kind of, already a presumption that we're doing it. So, just, kind of, trying to give both sides of that fence.

[Can social workers opt out?] 

Sophie: Yes, and I think that was really helpful actually, Mark, to think about it through that specific lens, because absolutely, you're completely right there, so thank you for that. Mairi-Anne, coming back and, kind of, pulling us back into the discussion around practice, a lot of questions around the critical thinking, as we've talked about, but I just wanted to pull out a question from Samantha here, which says, 'What if you don't want to use AI? Can social workers opt out of using it?’ They might have concerns about the ethical, environmental and social effects, which are huge. So just any reflections on that, and on practice, and what that, kind of, decision making might mean?

Mairi-Anne: Thanks Sophie, and thanks for the question, Samantha. So, before I talk about social care, let's just talk about general real life, and, you know, AI is absolutely embedded into the systems and processes we use all the time, right now. If you want to not use that, then you probably would have to not use any technology at all, because it is there. Whether you're aware of it or not, it's underpinning a lot of the transactions, a lot of the search functions, you know, all of the chatbots that you see on different webpages. So, AI is in lots of places at the moment. Can you withdraw from it if it's built into a system? I think… unlikely that you'd be able to say 'Well, actually, I don't want to use that.' And you know, just picking up the point that Amanda made in the chat earlier, why are we rushing headlong into the use of AI when actually, social work and social care has been slow in rushing into anything new in the past? So, why are we rushing into the use of AI without actually having a full consideration of the implications of it? And I think that while the consideration of these implications is happening in different places, it tends to be local authority based rather than having a broader, national discussion about it. So, for me, it's very much around that national discussion, about the, kind of, ethical framework for decision making around that. But, you know, in response to Sam's question, I think it's very unlikely that if your local authority has a system that is AI enabled, that you would be able to say, 'Actually, I don't want to use this,' and that would be ok. I don't know, Mark, would you have a view about that from a systems perspective?

Mark: I think you might have that choice now, I think we absolutely won't have that choice moving forwards, and that won't be something that will be of our doing. I think it will be suppliers will embed AI within solutions, if they haven't done already. We will either accept it or we won't accept it, but I don't think there will be an alternative at a certain point in time. But I don't necessarily see that as a bad thing, I think it's as long as you use it in the right way, is the critical piece of it.

Mairi-Anne: And I think, I mean, the bigger issue for me is the consent of people using services, because if the case recording is a reflection of their voice, or should be a reflection of their voice, then I do think they have the right to make a decision about how that's reflected in the case record, and to also think about the authenticity of that. So, for me, the big ethical question is around the use of service user data.

Sophie: Absolutely. There was a point in what you were saying there Mairi-Anne, that I'll just pull out a quote from Megan in the chat, which is just a statement rather than a question, 'Social work is so behind with the use of technology. We need to invest in research, development, and roll out of what would support the recording heavy requirements of social work.' So yes, thank you for that viewpoint, Megan. And I think that also, talking to the fact that AI is so prolific, so everywhere, Shannon states that, 'I've had what I believe is a significant increase in communication from parents and families that appear to be AI generated.' Like you said, AI has a voice, and it's often very evident that it has been used, and so, whilst there's a, kind of, full awareness of the positive nature of the fact that AI has given people from every background, and education and opportunity to communicate and express themselves, there is that pattern of AI use that's really prolific across all aspects and areas of life. There is one point that has come up in a few different places, and once directly, which is around the environmental impact. Before we wrap up and move to the close, any reflections from the panellists about that environmental impact?

Mark: Absolutely, the sustainability element of how we use AI is one of the key considerations that we need to think about. People quite often focus around energy consumption, water is hugely consumed by processing in AI as well. If we look at the usage that we're talking about, within the area that we are doing it, there is definitely an ethical view around what is right. So, I joked earlier, if I used the AI to create me the Olympic diving cats, is that really an ethical use? How much processing and computing are being used to achieve that? If we're using it to support a social worker to keep a vulnerable individual safe, then that absolutely feels like the right sort of thing to do. In the background, you do obviously have the big cloud providers, [who] are trying to create more efficient computer chips and those sorts of things. They're working to net zero and, you know, use of renewables. So, there is a reducing cost. In certain instances, it comes down. But there is absolutely human responsibility about how we appropriately apply it, and where we use it.

[Takeaways] 

Sophie: Thank you, Mark. Ok, I will pull the panel to a close, but just, is there one… just a few takeaways from yourself, or one takeaway, if you wanted to give the audience, as we do close the panel?

Mairi-Anne: Thanks Sophie. I hope that everybody's enjoyed the panel today, I certainly enjoyed looking at the chat and seeing everybody's comments and views, and certainly, lots of rich conversation there. In terms of takeaway, sorry, I have more than one thing - of course! And the first takeaway is that AI is here, it's here to stay, and it's how we use it that's where we have choice now. It's not whether we use it or not, really. But the, kind of, three things that I would really be emphasising is, remember the dual purpose of the case record, remember that the fundamental purpose of it is to record the child's voice. So, we need to do that with authenticity. And our professional responsibility is to do that in an ethical way, and that needs to drive the decision making. And I think the second thing is that using AI doesn't reduce that professional and ethical responsibility. It actually means that you have to be responsible for doing that, but I really like Mark's phrase there, about human in the lead rather than human in the loop, so remember, human in the lead, and that's the kind of principle that needs to underpin it. And one of the points that was made in the chat earlier was around whether AI will replace humans in different activities, and I think AI will definitely change the way we work. It won't necessarily replace humans though, but it might change the tasks that we do, and so, we need to make good decisions about where we need human interactivity and where it's the right thing to do, rather than just thinking about, 'Well, it will be more efficient and more effective, so let's just do it that way.' So, these are the, kind of, key points that I would leave you with. So, don't be afraid of AI, embrace it, but understand what you're using it for, when and why.

Sophie: Wonderful, Mairi-Anne, thank you. Sarah, over to you.

Sarah: Yes, my takeaway is around the lawful, ethical and responsible use of AI, and developing your AI literacy, so, for your specific context, so that you're able to do that. And I think that, you know, just to touch on a couple of Mairi-Anne's points there, it is here to stay, we are just scratching the surface of what AI is going to look like in social work. I think that the horse has bolted, and we need to think about what that lawful, ethical and responsible use looks like now, while we're using it for such a narrow part of our work. Because it's likely that it is going to advance out into other areas, like predictive analytics and using machine learning, and all of those sorts of things, a lot more. And it feels like we need to get it right from the start, and to think about what that actually looks like, start our own journeys for developing our AI literacy, and think about what our own responsibilities are within that scenario.

Sophie: Thank you, Sarah, and Mark?

Mark: So, I think I would say, nobody's got the answers, nobody's figured all of this stuff out. AI is absolutely a learning journey for all of us. Some people might be slightly further ahead, but we're all in exactly the same game. So, be curious, go and play with it, don't be scared of it, understand its strengths and its weaknesses, because actually, they are both and it is really important to understand where it's good and where it doesn't help, and be really challenging about that, kind of, appropriateness of where you use it. And as Mairi-Anne said, slightly stole my line, is you are the human in the lead, so make sure that you take that role.

[Outro] 

Sophie: A really big thank you to all of you for your time and your participation via the quick Q and A in the conversations, and a massive thank you to Sarah, Mark and Mairi-Anne for presentations and collaboration, discussions in the panel discussion, and a big thank you to Grace as well, for all of the support in setting up this webinar. With that, I think we'll draw the meeting to a close, so thank you so much all, and we've had a great session. Thank you.