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SheCanCode's Spilling The T
SheCanCode's Spilling The T
How AI can help women in tech
In this episode, Ruth Bucknell, VP Experience Design at Merkle, joins us to share her journey of building a career in the tech industry and navigating the challenges of male-dominated spaces. She reflects on the importance of intentional inclusion, her motivation for founding the Women in Tech network, and how community can be a powerful driver for change.
We dive into the ways tech products and AI systems are often shaped by male defaults—reinforcing bias through skewed datasets—and why correcting this matters for everyone. Ruth also highlights a positive path forward, spotlighting initiatives like Dove’s Ethical AI & Zero-Party Data Strategy, which is setting new standards for authenticity and representation in digital spaces. From banning AI-generated women in ads to launching the Real Beauty Prompt Playbook and the Code Campaign with Pinterest, she shows how rethinking AI can empower women and reshape the industry for the better.
SheCanCode is a collaborative community of women in tech working together to tackle the tech gender gap.
Join our community to find a supportive network, opportunities, guidance and jobs, so you can excel in your tech career.
Hello everyone, thank you for tuning in again. I am Kaylee Bokesman, the Managing Director Community in Partnership with AtheCan Code, and today we're discussing how AI can help women in tech. I've got the incredible root fractional VP experience design at Merkel with me today to dive into the ways that tech products and AI systems are often shaped by male defaults, reinforcing biases through skewed datasets and why correcting this matters for everyone, amongst a few other things that we're going to touch upon today. So, welcome Ruth. Thank you so much for joining us.
SPEAKER_01:Thank you for having me. I'm excited to have the conversation.
SPEAKER_00:Yeah, it's such a topical one as well. So we're so pleased to have you on here to chat about it. Can we get started with a bit of background about you to set the scene for our listeners, please?
SPEAKER_01:Yes, absolutely. So I actually started my career in the creative space. I trained as a designer, but it was quite broad, and I actually spent a lot of my time focusing on kind of tech-enabled design. So, how can we use technology to bring the creative space to life? When I finished studying, it was actually the credit crunch, and jobs were very, very sparse. So worked freelance for free within a number of different agencies, kind of racking up some experience, and soon figured out that actually I really enjoyed working with technology and looking at how technology can enable creativity. And you know, when I studied, UX wasn't really a thing, you know, user experience wasn't really a thing. Um, and so spent a lot of time in unpacking how humans um operate with machines, uh, whether that's at basic level websites or apps or digital products, and how we can um make a digital experience much more consuming and and user-friendly. Um, and then worked for a number of different agencies um with technology and looking at how we could then use tech to enable that, but making sure at every single point that what we're designing is very human-centered and have always operated in the the manner of you need to understand the unmet needs of today, but we have to focus on the unimagined futures of tomorrow. Um, and working in in that space is is quite exciting. And this is why I actually love what we do because you know, who knows what this is all going to look like in two years. I mean, four years ago, AY wasn't no, you know, mentioned, and now it's in every single conversation, every single pitch. Um so yeah, it's it's transforming quickly.
SPEAKER_00:Yes, I love that. And and you can hear it, I can hear it in your voice, but you can hear it in people's voices in tech when they talk about how fast things change and why they enjoy working in this industry. And it's it's almost a shame, it's like a hidden secret that people don't know until they get into tech because they have that misconception of working in the tech industry. Um especially now with things like AI, how exciting that can be to work in. I love the fact as well that you you refer to it as creative because a lot of people uh they think that tech is going to be quite boring and quite rigid, and um, you know, you end up in a in a in a job where you don't get to be that um innovative in in your role. Is that something that that you did you have that opinion of the tech industry before? Did you really think you know, back when you were at school thinking, oh, if somebody had said I'd be in tech, you know, years later and doing what I do, it just wasn't something that crossed your mind at that point, and that your role would be this creative.
SPEAKER_01:Yeah, absolutely. I mean, when I studied, IT wasn't even, you know, something that you studied at at school. I you know, we grew up with the stereotypes that dad would fix everything in the house when it came to technology. Um, so I didn't necessarily notice that bias, but I do think that it was definitely in me. And then even when I started out in the industry, you know, even developers were like, oh, those are the ones that stay in dark rooms with, you know, the the blind shut, you know, there was a very clear stereotype. But actually, I thrived with working with developers and people who could enable the vision that I had, because I do think if you bring creativity with technology to life, you can create some amazing, amazing things.
SPEAKER_00:Yes, definitely. And and it is like uh you have that misconception about certain roles, and then you get in and you think, Oh, I didn't know actually it was going to be like this. And the more that we share stories like yours and what other people were doing in tech, the more it inspires other people to think, actually I could do that. Or I have lots of people um in our community who have um uh said that you know they were inspired by somebody else that was that was doing it, they really didn't realize what it was going to be. Um, and so many of our community are career transitioners, they've been in work 10-15 years already, and then they realise they have all of these wonderful skills. A lot of them come over as project managers, for instance, and they go, actually, I didn't know I could do that in tech because I already do all of these things anyway, and a lot of their skills just transfer straight into the tech industry, um, and then you find yourself in an industry like you said, that is just really fast paced and moving forward and you land in the right company, then that that's it.
SPEAKER_01:Exactly. And you know, I always talk about the type of people that we hire, and I want curious individuals, people who are always asking questions, trying to unpick the next challenge or or come up with a solution. And one of the things that we're really seeing with AI is it's bringing closer the design skill set and the technology skill set, especially from a front-end development perspective, because now the designer has all of the tools within their kind of suite of of tools to be able to actually bring their ideas to life super quickly. It's so easy to vibe code something. Whereas in the past, you would have, you know, a static design that might have been done back in the day, you know, in Photoshop or InDesign. And now, you know, everybody's using Figma, and now with a click of a button, even in Figma, you can develop a prototype, but now I can even go and vibe code something super quickly, get all of the code, and then give that to the back-end developer to you know stitch in with some other APIs. So it's it's really starting to see us embracing a slight role shift and bringing the the two capabilities massively together.
SPEAKER_00:Yes, I love that. Um, and I wanted to ask you because obviously you've built your career in a mal-dominated industry, um, which um would have come with lots of unique challenges going into tech as its own challenges anyway. Um, but can you share some of the challenges that you faced along the way and what intentional inclusion has looked like for you um in practice?
SPEAKER_01:Yeah, absolutely. And I think it's funny looking back because at the time I don't think I really realised it. I just thought it was a status quo, it was the norm that I was, you know, in face myself in meetings where I was maybe the only female. And I actually at the time I was less conscious about my gender and more conscious about my age, which is interesting. That I thought, or maybe, you know, these these guys have been around for for years, they've got a wealth of experience, you know, they're not gonna be interested in, you know, little old me. So it was it's funny that actually gender at the time never really crossed my mind. I was much more conscious about the wealth of experience that I maybe had rather than these days. I really encourage younger generations to challenge, you know, our thinking because there's you know much more interesting ways of of delivering things that maybe we wouldn't consider. So I say that that's um a huge shift, but yeah, I noticed that as I started to progress that I was when as a as an industry we started to become aware that actually we needed to promote um more female advocacy within, you know, the the tech space. I remember suddenly being included very last minute in many pictures where it's like, okay, um, it's clearly just you know, a load of men sitting around a table. We need we need some female blood in here to kind of show that we are a diverse group of individuals. And I remember thinking at the time, I want to be included for my visionary thinking, my thought process, the diversity of you know, thinking that I bring, rather than being brought in right at the last minute because I fit the the gender requirement that we we have. And I remember actually speaking up um at the time and and declining to be part of a couple of pitches last minute because I I said, you know, I I want I love growth and and pitching and coming up with ideas, but I want to be involved throughout. I don't want to just be involved at the last moment. And that's where I don't know, something switched in me that I suddenly became very aware of why I was being included. But actually that gave me a voice, and that's why one of the reasons actually why we wanted, and you know, I I started the Women in Tech Network is to try and you know stop some of those, those challenges that many faced and and quite honestly still do face. But I also had um some great sponsors as well um who put me in situations that were slightly outside of my comfort zone, but they believed that I could um have a voice. Um one of those um was um who's actually now uh our CMO at Denzu, um, a man called Aslan Raj. Now he and why he really sticks in my mind is because he wasn't somebody who just said, you know, Ruth, I'll put your name in the hat for that, you can do it. He then really coached me through the opportunity as well, making sure that throughout I was, you know, I was comfortable, I had some pointers, some steering from him. And that's something that's really stuck with me from uh a sponsorship perspective is sponsorship isn't just a label that you should you can give somebody, it's it's actually providing them the opportunity, but really coaching them through it so that they succeed and that they can go on to the next one.
SPEAKER_00:Yes, definitely. It it isn't just a label. I think a lot of people say they they are sponsors or mentors or um coaches of people, and it's not necessarily that useful or what you're looking for for someone. It's so interesting that you mentioned your age and experience and over gender. And we have quite a few people uh approach us when when we're at shows, for instance, and we get students or career transitioners, and they say, How do I how do I move forward in an industry without the experience? I mean it and the experience and showing what you can do when you don't have that yet, for instance, if you're still young, it really does worry people. And I think in tech, that's almost worse because you get into tech and people use a lot of jargon, and you find yourself in a meeting where you're like, I don't understand what's happening, imposter syndrome kicks in, and you sort of start have that feeling of, am I meant to be here? And it can be quite intimidating to be in those meetings with people that are very experienced, and and that unfortunately does cause a lot of people to think I don't want to be in this anymore. Um, so I suppose sometimes as well being in the right company where age doesn't matter and you are still included and encouraged to, you know, you have to find that experience along the way. Um, some companies are very good at that, as you said, coaching people along the way instead of just saying that we're helping and not actually helping those people that might be sitting there having those worries, like, what's everyone talking about? I don't have this experience.
SPEAKER_01:Absolutely. And it's super interesting, right? Because AI has suddenly made information accessible to all. So experience used to be about the number of years that you had as a developer. Whereas now, with uh you know, access to you know, AI tools that anybody in an entry position has can code super, super quickly and and easily because it's so readily available. And even people without a tech background can also start to um play and interface with technology solutions because you know it's very easy. The interface now to code is very human-centered, and so AI, in a way, is really starting to shift how um younger audiences can enter and challenge the status quo. Now, you you do need experience, life experience more than anything, of how to handle client conversations or or challenging projects, and and that does come with experience, and that's not something that AI can replace. But I um read a study recently um where um the the leader of a team gave a challenge of I need you to design a um a new digital product, and you've got two weeks to design it. And he gave the task to three individuals: somebody who had 20 years experience, somebody who had like eight to ten years experience, and somebody who was like like two, two years experience. And two weeks later, the the person who had 20 years experience had come back with 20 screens that they'd designed. The person with eight to ten had come up with um a prototype that they'd they'd made, and then the person with two years experience had vibe coded a whole interface and had done it in half of the time. Oh wow. And so if you take it on face value, that actually all of the concepts were the same level of quality, it just shows you that actually there are new tools that younger generations are now experimenting with that are really showcasing like fantastic talent and thought process that if we take that you know experience and pair it with the the enthusiasm of some of these these tools, then actually we're you know, it's it's gonna be quite powerful. But it but that's for me a great example of how you know the younger generation can and can shift things up.
SPEAKER_00:Yeah, well, that's such an interesting test because you would think, yeah, actually with the quality, yes, somebody with all those years' experience might be better in terms of quality, but they they're also always gonna have that thought of but this is how I've always done it. I've always started this way or I've always been through this process, and then people with less experience just cut to the chase, and because they don't they don't have that process in their mind yet either. They just thought, what's the easiest way I can do this? And and and again, that comes down to diverse teams, you know, not just with gender, but different experience levels and people different people dipping in. And if you've got all of those people and you think, oh, we've got such an experienced team, everybody's got 20 years of experience, and you're all doing the same thing and not necessarily moving as fast as some other younger fresh minds might bring to the table. Um I I love that. I haven't heard that experiment before. That's um that's that's a brilliant example. And I wanted to ask you, you mentioned the Women in Tech Network um uh that that you launched. What what what inspired you to found the Women in Tech Network and what impact have you seen um it has created for women in the industry so far?
SPEAKER_01:Yeah, um so initially it was really noticing the kind of lack of representation and role models, to be honest. I had no one to look up to um from a female perspective in the tech landscape, um, especially in kind of more of the leadership and and engineering roles. Um and I also, as I mentioned, noticed that there were few women um in the rooms where technology decisions were were being made. Um and also another trigger for me um was having a daughter. And I suddenly started to think about all of my experiences growing up. Like I mentioned, you know, my father being the only one who could fix, you know, the TV when it wasn't working. And I thought that actually the exposure that we give to our children when we're young is is the most important to show that, you know, girls can do anything. Um, and it doesn't just have to be, you know, um a stereotypical like male, male job. So that was um a big trigger. Um, and I founded it with my um partner in crime, a woman called Sarah Painter at the time, and the the um the objective was really to create a community of visibility and and that support network where men and and not just sorry, not women and men, I think I want to make that point that um I've been part of so many female in leadership um programs, and they are never successful unless you include men as part of the process, because if not, it's a complete echo chamber of women speaking about the same challenges to women, whereas actually to get that sponsor or to get that seat at the table, you need men and those who have um access to realize the seat of power that they're in to invite you in. So that was um something that was hugely um important to me. And then the other trigger was um I've worked with many global teams um or teams around the world. So, you know, we've had teams in Jordan or India, and you know, we've got a fantastic team in Bulgaria, and I was started to notice that actually in some of those other areas uh uh in the world that we were working with, they had a much higher female um intake. Um so and then I started to do some digging to be like, why are some countries thriving? And then others, quite honestly, in the Western world, we're we're falling behind. And you know, like Bulgaria has the best um male-female ratio and ICT sector within the um within Europe. Jordan, they have like 45% of women study computer science, um, which then showcases like an incredible pipeline of future talent. And even in India, um, 34%, so it's not a huge percentage of the IT workforce are women, and most of that actually sits under 30. So again, you start to see a bit of an age challenge as people progress, maybe into you know, the years they're considered like the motherhood years, which which is another challenge. So it was a mixture of underrepresentation and lack of leadership that I was witnessing, but also inspiration of different cultures that were um, you know, facing different challenges, but actually female representation seemed to be much, much higher. Um, so the goal really for us, like I mentioned, was to create a safe and supportive space for women to connect, share, and have that exposure of actually, you know, other cultures that were outperforming us and what could we take to learn and and embed within you know the power that we have within Merkle to start to challenge that.
SPEAKER_00:I love that, especially the different cultures. We're very lucky on spilling the tea, we get to talk to lots of different people around the world. And when you ask them about like what they wanted to do when they were young and and what they studied, and you'll get certain people from certain countries who will always say, Oh, I always wanted to do STEM subjects. I I of course I wanted to do computer science, and loads of people in my family were doing it, and that was a great inspiration. And then, like you said, you get other people, mostly in the West, who will be like, I didn't really have any role models. I fell into tech, and you know, I was in work 15 years already and luckily fell into it. Um, but it's so interesting to hear where people get that inspiration from because if there's no one at school encouraging you, or if there's nobody in your family that's doing it, um, then how do you find your way in? I've had lots of community members tell me that their husbands inspired them, and a lot of that happened in lockdown because they were working at the kitchen table and noticed their husband was a scrum master or a developer, and things like I could do that. And a lot of our community members said that they actually retrained and came into tech as a result, and sometimes it's just seen someone in action, somebody to inspire you, because like you said, there is a lack of role models, unfortunately, especially leadership roles. Um, and suddenly you'll see someone doing that, and you think, I could I could do that.
SPEAKER_01:Yeah, absolutely, and that's quite so the the thought process behind the Women in Tech Network was that we had like three pillars that we wanted to really focus on. One was really inspiring the next generation, that's key. It's not the people that we necessarily work with, but it's offering that inspiration to women. So we worked and partnered with um uh people like Girls Who Code, where we then had an intake of young females from um schools, and then we also did a program with universities to show them how you know you could enter the tech the tech space, which was not only inspiring for them, but really inspiring um for the community within Merkel. The other pillar is improving representation by focusing on more recruitment and retention. So, from a younger generation perspective, we're actually seeing this split between male and female um improving. We start to see some challenges as women enter kind of um the the years that are considered often like the motherhood years, where then suddenly there is a bit of a gap still in the leadership positions, which is something that we're still trying to tackle. But really importantly, as I mentioned earlier, is about empowerment and allyship. So upskilling not only women, but fostering like male allyship and really sponsorship to reach gender parity is is key. Yes.
SPEAKER_00:And how to how to let men know as well that how they can get involved. We have a lot of men who ask, they they want to come into our community and show their support and they're wondering how. So we always say, you know, we have a mentoring program, come in, um, be part of that. We have lots of male allies in the directory in there, but men don't always realise you know, allyship is a bit more than saying, yes, I'm in support of women in tech. It's like where can I get involved and how can I show my support here? Um, because they might have seen uh good things happen in in their own companies and can see the benefit of um of leaning into that a bit more. Um I wanted to ask you, uh uh pick your brains a little bit about um AI and um the fact that a lot of AI systems are built on data sets that reflect mal defaults. Um, can you give us some real world examples of how that bias shows up in tech products and why it's important to address?
SPEAKER_01:Yeah, absolutely. So I did a bit of digging around this to make sure that I kind of bring some facts and figures to you. But even I notice it in voice assistants that we speak to. Early versions of like Siri or Alexa, they've always defaulted to female voices and respond quite submissively to questions that have been asked. So I think even subconsciously, that role of providing information is taken on by the female voice. Now, if you then start to do a bit more digging and start to think about the models where facial recognition has been built. Um, so and what we've seen is that um systems are showing a much higher error rate for women and people of colour, sometimes even completely misidentifying them entirely, because it shows that the data that they're looking at is not as maybe inclusive as needed. Um, so for example, um for there's a couple of studies that have been done um in the US that show like the error rate of light-skinned men was about less than 0.8%, but the error rates for dark-skinned women were as high as 35%. And in some areas for like the darker-skinned women, error rates reached nearly 50%. And that root issue is really in the training and the evaluation of data sets which are heavily skewed towards lighter-skinned male faces. So that gives us, you know, something to consider that AI is not um is it it's reliant on the quality of data that it is ingesting. So I think that we all have real responsibility about making sure that the data is inclusive and representative of the real world. Um, and that, you know, it has huge impact because um, you know, if you can misclassify people based on like intersexuality and their traits like gender and race, we then start to see default errors, and that has huge real what we're real-world consequences. So, for example, like surveillance and misidentification and exclusion. So that's I find something um incredibly um interesting. And then I don't know if you remember, there was um uh Amazon used an AI tool for all of its recruiting. Um, it's around 2018, and they disclosed that as they used this internal recruiting algorithm that helped personalize resumes um and then exclude certain resumes, and it had excluded many um words that um and that had were linked to female trades, even things like women. So they would exclude CVs if it could include something like women's chess club or something like that, because historically male applicants had always applied, and again, the data was based on all of those um CVs or resumes that they had consumed previously that then showed a huge bias towards potential new applicants that could then um showcase that diversity. Um and even, you know, so so I suppose the takeaway is it's not, yes, it's it's looking at AI, and I'll just come onto that in a second, as in the kind of the tools themselves, but firstly we need to make sure that the data is right, because if not, there's no hope. Um and then the other challenge we have is that actually because we have such uh a male-dominated industry in the development of tech and AI, that all of these tools are being built by predominantly men, and therefore there is this natural bias. So it's our responsibility to ensure again that we have women entering this space that can also show that diversity of thought and perspective in developing these tools to make sure that we don't have um that that bias that's the kind of inherent in in the product itself. Um so yeah, it's it's super interesting, and I think it's something that we're becoming more aware of. Um, but it's definitely, you know, 2018 with the Amazon AI fiasco isn't wasn't isn't that long ago.
unknown:No.
SPEAKER_00:No, actually it's not. And and you think interview processes, a lot of companies have been working on them and and how to make sure that the you know um you get a more diverse talent call, for instance, coming through. And then if AI comes in and it almost as completely forgets as well everything the companies have been doing. I remember um uh being told about an orchestra who uh always um had lots of male uh um musicians, and the way that they got around it was they they started um blind auditioning everybody and there were still too many men getting through, and they was like, but is this something that we're doing? So they made everybody take their shoes off when they were doing it. So as you walked across the stage that you couldn't quite hear whether it was a male or a female, and as soon as they done that, more females were picked to be part of the orchestra, and it's like certain places know that that is inherent and they're they're um taking steps to make sure that they can improve that. If then AI comes along and then starts to erase some of the things that have been happening, and those sorts of mistakes happen, like with Amazon, um, it's not useful because the pipeline just gets smaller, those people just don't make it through to even a first interview, um, if that data isn't correct, as you said.
SPEAKER_01:Absolutely, and it's really interesting when we talk about bias because um there's also this other recent study that was done auditing LLMs, so specifically um ChatGPT, I think it was like 3.5 and 4. Um, and they were measuring the self-preference bias between AI systems, and what they were seeing is that there was a tendency for AI systems to favor CVs generated by themselves rather than human-written ones. Which is so now not only do we have the bias between gender and maybe age, but you now we're developing these tools that are starting to understand the the way in which they, you know, um represent themselves and are now favoring AI-generated solutions rather than the human ones. So it's we're now entering a huge space that we just don't even know quite how to match.
SPEAKER_00:That is so interesting. We were actually talking about that um with somebody who helps us with content recently, and uh he was saying there are obviously lots of AI-generated um articles, but the human ones are still performing much better and traffic-wise. But that's interesting because if uh people start putting out AI-generated content everywhere, will eventually Google start you know changing its algorithm and thinking actually, I prefer those over the human ones and the tide could turn. It's so interesting how fast things are moving.
SPEAKER_01:We're seeing it massively with pitching as well. So many procurement teams are now using AI tools to go through pitch responses to give that kind of first review of have these agencies answered these questions in the right way? And so again, if there is a preference for AI tools to kind of favor them themselves, is it it's you know, start you have one, have to be very careful about using AI in your your answers, but also if you then start to see this bias appear, it's like you know, do you have to play the game? Yes, slightly.
SPEAKER_00:Definitely. And um, so Dov's ethical AI and zero party data strategy, um, that's a refreshing example of how companies can push back against bias. Um, what aspects of that initiative do you think are most groundbreaking for women in tech and beyond?
SPEAKER_01:I mean, Dove have such a powerful brand that they can make such a great statement for women. And for those of you listening that haven't heard about the campaign, um, Dove noticed that there are many AI tools that favor um or or have a view of what beauty looks like because it is being programmed predominantly by men. But actually, that isn't a reflection of what real beauty looks like. So they partnered with Pinterest to create um uh a really inclusive quiz which was retraining the AI algorithm behind Pinterest of what beauty looks like. And I just think that's hugely powerful. So as a as a person, you could go through and select, you know, out of two images what was more beauty, you know, somebody who had no freckles or somebody who had loads of freckles, you know, lots of lots of different considerations, um especially around um skin colour, body shape and size, um, you know, height, everything. It was it was really thought about every aspect of you know of beauty. Um and um one, I think it was a powerful statement to make, but also it was a great way from a business perspective to then really start to collect first-party data to then be able to kind of target and go back out to to women. So um for me, it was a super powerful um way in which brands can start to pivot and consider um different ways in which they can use the tool to their their advantage and take a stand. But I do think there are some counterexamples out there that we also have to be aware of. And actually, going back to your your comment around content um is something that's it's it's very much on my mind. And I don't know if you remember during Wimbledon, there was an influencer that was created, Mia Zulu. And no, she was um she was all over the press, but she was an AI-generated influencer.
SPEAKER_00:Oh, yeah.
SPEAKER_01:And she was, you know, I mean, she was beautiful, but beautiful to a point of like, could any person ever achieve that level of beauty? And many of the photos massively duped people, but it wasn't real, yet many people believed it was. And then this synthetic persona sparked huge debate around authenticity, manipulation, and trust in digital environments. So this also raises loads of red flags where we need to consider synthetic personas that are presenting this ambiguity and risk really blurring the lines between the power that influencers have with their real voice and their real opinions and making sure it's authentic versus the manufactured ones. And I don't have an answer about how we police that, but again, it's something that we really need to address, um, especially in younger audiences, because social media, you know, as we know, has causes huge issues in, I mean, even probably our generation, but in much younger audiences. So we we do need to consider the ethical impact that we are are having. Um so yeah, it's kind of for me about like the transparency and disclosure around it.
SPEAKER_00:Yeah, and always remembering as well to point out that that's not the norm. Like you said, that was just those levels of beauty, I mean, they might exist, but they're incredibly rare because normally you look at Instagram and you're like, everybody else is you know successful and beautiful, and how do people look like this? How are people doing this? And we're very um it's the same with um the stories that we like to share in our community. We always encourage people to share what and all, how you found, how you, you know, especially if you launched a business, like what happened with all of the the things that went wrong as well, because they're the things that want that people want to hear. If you're always presented with this perfect person and perfect life and on Instagram, and then when people actually try to do that, whether they do want to launch a business or do want to look a certain way, it's just unattainable. And and like you said, there are there are so many failures around social media anyway, and and how we digest all of that content, but it it has become a lot worse with AI and what is being generated that we're we're having to see every day, and then think, yeah, my life is nothing like that.
SPEAKER_01:Absolutely, and that's what why for me Dove's campaign was so powerful, because it looked at not only how um shaping how AI is used, but also making sure that it's not passively being shaped and it's being used for something for the the greater good, which obviously aligns with their brand values, but I think hearing the real voice and the real human um and and seeing that is much more powerful. And and again, it's something as as women we want to see because we want, you know, we don't want to compare ourselves to something that's just unattainable. Exactly.
SPEAKER_00:Um so looking ahead, what excites you about AI's potential to create more inclusive workplaces and technologies for women? And what still worries you?
SPEAKER_01:Oh gosh, what what a question to finish. Um loaded question for you. Yeah. Um, so I think AI can really help us remove certain barriers and it democratizes access to information. So it actually puts people on a leveling playing field in terms of you know, information and starting out. So, you know, earlier on we talked about the age challenges, whereas actually now, sometimes if you're younger and are very fluent with this technology, you actually have much more power in your hands. Um, so it's really removing some of those traditional gatekeepers that we we we had. It's also giving us um hugely like inclusive design opportunities to kind of challenge the the status quo. So think about you know the the dove example that we that we spoke about. But also AI, um, and definitely as a female, and I look at this in um some of the challenges we have with more um senior women in leadership, is that as women who have multiple hats that they need to wear, whether they're a parent or a carer, you know, we're entering, well, I'm certainly entering in that era into my life where I need flexibility and hybrid working to be able to be successful. Um, and I think AI is allowing us to kind of personalize those experiences well and allow us to have more of that work-life balance. So I think that's really helping us strive and even thrive in the workforce. Um and I think it's an exciting era. Like I said at the beginning, there are so many tools that are now accessible and the interface is so accessible that actually it's making, you know, anybody can design uh a website or a digital product or code something because those tools are are accessible. So we need to think about how we police and and and you know um are you know offer that kind of gatekeeper of authority around how the the tools are used. But I think that that's um in some ways it's becoming so much more accessible, which then starts to just level the the playing field. Um in terms of challenges, gosh, there are so many because I would say um AI, you know, it's it's popping up in every single conversation, but I think we're pretty nascent still in how it's it's being used. Um so I don't think it's the the kind of golden you know bullet to to everything. We've spoken about how there are huge gaps in the data, um, which is something that we have to address in terms of making sure that actually the the results that AI um is providing is inclusive and representative of the world in which we we live in. And that's making sure that we start to encode or decode some of those biases to make sure that it kind of persists. Um and I think that we a real consideration is around the ethical challenges. So we talk, you know, we talked about mere the AI influencer and how do we safeguard around that? How do we actually create a space for real human content and experiences to live versus taking advantage of some of the AI tools that can can help us get there? So I think we're yeah, pretty nascent in understanding how we regulate and and offer that kind of transparency.
SPEAKER_00:Yes, definitely. And as she says it because things move so fast. I I've spoken to a few companies about AI, and they said with them it's it's those guardrails, like you said, it's just making sure that as you move through this journey that those companies are putting them in place, that they're aware as well of you know, things like the recruitment process, what it's gonna do to your recruitment process if you use AI, but don't put any guardrails in place. So, and how you're gonna go through this phase where you're actually probably gonna go backwards on what you were trying to achieve, um, if you don't try and get ahead of um how uh how AI is is for instance selecting different candidates for you, um, or always ensuring that and and I've heard this from a few people, AI is really speeding things up and it is helping people, but you always still need that human influence to ensure that somebody is dipping in and and checking on things, um, and uh just to ensure that you're still moving forward. So it's a good mix of both at the moment and not kind of heavily relying on AI because we uh as you said, we're not quite there yet, and those challenges are going to be with us for some time. Um but we are already out of time. I could keep talking to you about this topic for ages. It is so topical, it was so interesting because there was so much to unpack just in this small area of the tech industry as well and what companies are doing. Um so thank you so much for taking time out, Rufie. It's been an absolute pleasure having you on here to to um to share your thoughts.
SPEAKER_01:Amazing. Thank you so much again. I've really enjoyed the conversation.
SPEAKER_00:Thank you. And for everybody listening, as always, thank you for joining us, and we hope to see you again next time.
