top of page
Search

Bridget Boakye on AI Policy Between the UK and Africa

In this episode we talk to Bridget Boakye, the artificial intelligence policy leader at the Tony Blair Institute for Global Change. Bridget is an expert in how AI is impacting Africa and the major challenges in implementing AI use across the continent. She tells us about what good technology means in the contexts in which she works and the benefits and drawbacks of Google and other big tech operating in Africa.


Bridget Boakye is a Ghanaian entrepreneur, data scientist and writer. She is.the Artificial Intelligence Lead in the Internet Policy Unit of the.Tony Blair Institute. Bridget’s work focuses on internet policy, start-ups and innovation in Africa, AI ethics and resetting the global narrative of Africa through tech. Her previous work includes data science and analytics, and business development and strategy. She co-founded TalentsinAfrica, one of Africa's fastest-growing skills accelerator and recruitment platforms. Her company was among the top 20 companies selected in October 2019 for the Harambe Entrepreneur Alliance at Bretton Woods, New Hampshire. Her company also emerged as one of the top three start-up companies at the Oxford University Africa Innovation Fair.


READING LIST:


Reaping the Rewards of the Next Technological Revolution: How Africa Can Accelerate AI Adoption Today (2022) https://institute.global/policy/reaping-rewards-next-technological-revolution-how-africa-can-accelerate-ai-adoption-today


TRANSCRIPT:


KERRY MCINERNEY:

Hi! I’m Dr Kerry Mackereth. Dr Eleanor Drage and I are the hosts of The Good Robot podcast. Join us as we ask the experts: what is good technology? Is it even possible? And how can feminism help us work towards it? If you want to learn more about today's topic, head over to our website, www.thegoodrobot.co.uk, where we've got a full transcript of the episode and a specially curated reading list by every guest. We love hearing from listeners, so feel free to tweet or email us, and we’d also so appreciate you leaving us a review on the podcast app. But until then, sit back, relax, and enjoy the episode!


ELEANOR DRAGE:

In this episode we talk to Bridget Boakye, the artificial intelligence policy leader at the Tony Blair Institute for Global Change. Bridget is an expert in how AI is impacting Africa and the major challenges in implementing AI use across the continent. She tells us about what good technology means in the contexts in which she works and the benefits and drawbacks of Google and other big tech operating in Africa. We hope you enjoy the show.


KERRY MCINERNEY:

Brilliant. So thank you so much for joining me today. So just to kick us off, could you tell me a bit about who you are, what do you do and what's brought you thinking about gender, feminism and technology?

BRIDGET BOAKYE:

Thank you for inviting me to speak on the good robot podcast, Kerry, I'm really grateful. And I'm such a fan of your work. So glad to be here. For my side, I am the artificial intelligence policy leader at the Tony Blair Institute for Global Change, which I’ll refer to as CBI if that's okay, where I lead the core group where we think about analysing and designing practical advice to governments on how to harness AI to deliver for their citizens. So my work with with feminism, gender and technology, I think, has roots in my undergraduate training, more so than perhaps anything else. I got a degree in Economics with a lot of the concentration on African Development and History. And I got this degree at a very progressive school called Swarthmore College in Pennsylvania. And at the time, and even today, it's sort of known for its social-justice orientation. So there I had an opportunity to really think about the issues around specifically, let's say economic development, gender, feminism. And the technology-side came a bit later on, when I worked in Media Tech in the startup ecosystem in Ghana. And as an African woman, sort of, you know, a lot of my work during that time was business development, before I got into the technical side of AI was business development and sort of having to lead a lot of our engagement externally on technology, I had to definitely think about what that means for me within the context of that space, a generally a male dominated space at the time that I moved back from the US to Ghana. So with all of that said, I am glad that today I get to professionally still engage with this topic, I kind of have this training in this space, I have the school orientation. But you know, luckily today, my in my work in tech policy, I still get to think about feminism, gender, and technology. And I'm glad that I work at an Institute that has dedicated resources to both. So we have an African women in AI sort of initiative, which birthed an African woman in AI Summit. And then we have our broader women in tech policy initiative. But I'll stop here in terms of introduction, Kerry, and I look forward to highlighting more about this programme, specifically the summit through the course of our discussion.

KERRY MCINERNEY:

Amazing there. Thank you. And it's amazing to me like how you mentioned that you move between so many different spheres, and then bring all the strings from these different places into the work that you're doing. Like, I find that really wonderful. And a bit strange for me to ask this question for our lovely listeners, Eleanor is currently stuck in transit somewhere, possibly in Germany. So it’s just me interviewing Bridget today. But Eleanor usually asks here, our podcast is called the good robot. So our million dollar questions: What does good technology? Can we have good technology? What does it look like? And how can we work towards it? And specifically, how does feminism help us get there?

BRIDGET BOAKYE:

Yeah, this is such a great question. And I think it's a question I wish more of us asked within the context of our work, whether on the technical side of technology, or the policy side, or wherever we kind of meet ourselves in this space. For me, I think good technology is simply technology that allows people to live and sort of operate within their social and cultural contexts. And in this way, good technology supports both human and nonhuman objectives. And a lot of my thinking around this actually comes from a Professor at MIT called Chakanetsa Mavhunga. And he's done some work on sort of African history and technology. And he argues that the common framing of technology is something that's built in labs or factories leaves a lot of the technologies built in Africa in the Global South, out of the current discussions about tech. And I think that's really, really important. The way we define technology ultimately defines who we think is in this space, what it looks like, and how we move forward in terms of what we want technology to look like. So for me, looking at technology within this broader context, allows me to think about technology beyond digital technologies, and in many ways to see the technologies built in many times in villages and forests and some of the non-stereotypical places that we find technologies and Africa is where they truly are, and to to look at what they have to offer in terms of insight to the world. So taken from this definition, I think the second part of the question is, can we have good technology? And I think we do, when we look at it more broadly, what do people use to allow them to live and operate within their specific contexts? One example that comes to mind for me, I'll use this example because I think people may be familiar with the film, it's called The Boy Who Harnessed the Wind. And the movie is based on the true story of a Malawian boy who uses materials he finds in a garbage dump to build a windmill to pump water from the ground for his family's cornfields during a drought. And you know I see a lot of this where I live in Accra, Ghana, where people are reinventing trash and waste and turning it into a resource. And to me, that's good technology. How does this allow people to live and operate within their current circumstance and make meaning of their lives? So when you say, you know, how do we work towards good technology, I think fundamentally to empower people with the knowledge, resources and funding to do that, to build these technologies that are useful for them. And you asked, How does feminism allow us to do that. And when I think about feminism, at least, you know, I am not an academic. So not having studied it in that in the pure sense of the word, the way I see feminism interacting with this in my lived everyday experience is that feminism says that we should all have an opportunity to live within the context of our identity. And I think when we define technology more broadly this way we allow, and we define, and we give space for people to design things that are meaningful for them in their specific area and their specific contexts.

KERRY MCINERNEY:

That's amazing, so many, just rich and fascinating ideas. And, you know, I love this idea as well, you know, that rather than overly intellectualising feminism, like it's this vast and broad practice and something we live in our lives. And so something I know Eleanor and I are really keen on in the podcast is to think about the diversity of feminism's and how they interact with one another. But also thinking about how this feminism allow ways of imagining that the world can be different and finding different ways to coexist with each other. And so I think shaping it that way around technology as well, allowing people the space and the room and the resources and the time to make the technologies that they need. That’s really wonderful. I actually want to come back to something you were saying at the beginning of that answer, though, which is specifically the framing of technology, something that comes from the West, from labs, from factories, and this dominant kind of racial framing, and the way that that ignores long histories of technology innovation outside of those areas, and I want to focus specifically on the western-centricity of a lot of AI ethics work. And so one way to counter the often exclusionary bent of AI ethics work, which often ignores people's voices and experiences from the global south of the majority world is by foregrounding the very long history of responsible innovation by African women in areas from technology through to climate change activism. So could you tell us more about this history of responsible innovation and how it can and should transform the field of AI ethics?

BRIDGET BOAKYE:

Yeah, thanks. Thanks again for that. And I love how you sort of take all of what I share and reframe it in such very interesting ways and questions. So in terms of this question about AI ethics and the Western-centric ideas and notions around it. I think it's important for me to reframe the definition of AI ethics from the policy side, right? AI ethics sort of lacks meaning when you bring it to the practitioners to the policymakers, the word itself leaves a lot to be defined. So when we look at it in terms of responsible innovation, I think the term - that term, is more nuanced and rich and allows people to kind of come to it with their own understanding and perception. I study and really appreciate the AI ethics field, but I sort of veer away from using that in my everyday interaction or parlance with some of the stakeholders that I work with, I think responsible innovation gives us a lot of room to think more broadly about the issues in the space. So with that said, I hope that definition allows us to bridge the gap between what we mean by AI ethics and responsible innovation here. And I think African women in particular have been, have been the forebears of responsible innovation. Not only in Africa, but around the world. And a lot of the work I do in this space is really about reframing and recreating a narrative. Because when you search, when you search, you know, when you do a Google search on the African woman in AI ethics, you see a number of initiatives that are intended to foster the work of women in this group. But a lot of what's missing is about the past or the existing work that's been done. And there are so many incredible women from Timnit Gebru, Joy Buolamwini, in terms of African women who are still doing the work in AI ethics and responsible innovation. But for me, given my economics background, and sort of my orientation and training on the policy side, I think bringing in the history of what the long history of work, responsible innovation by African women is or has done helps enrich the field by providing both content for what responsible innovation could look like and it empowers more African to want to contribute to the field. Because oftentimes when we talk about AI ethics is something that's Western-centric and presented as foreign to many young Africans in particular looks like something that is not for them or that they can contribute to. So that’s a long way around this, by first suggesting that we look at ethics more broadly as responsible innovation. Because it's not just about AI alone, it's about technology more broadly and short of innovating, and allowing people the space to create more responsibly. And secondly, it's about thinking not only about what African women are doing now, presently, but also how the history of it empowers people to want to do more to see themselves in the space, and also to provide ideas about what the space can actually look like. So I'll end by giving two examples of the women I love to cite in my work, especially when I talk to Africans about this, because they, they sort of know these people and it allows them to feel very familiar with the idea of responsible innovation. So the first one is Wangari Maathai, I think some people may know of her, she's a founder of the Green Belt Movement. She's also the first African woman to have won a Nobel Prize. And she, I believe she received her award in 2004. And in terms of innovation, a lot of people know her for her activism. But what she really did is, at a time when the conversation on sort of climate change was not really well pronounced - we're talking about the 1970s - she brought this idea within the Kenyan context to develop a holistic approach to environmental sort of restoration by combining environmental conservation, with community development, with capacity building, in a way that allowed her to foster community buy in. And this allowed her to make incredible, incredible contributions to this space that were celebrated not only in Kenya, but around the continent and around the world. If I can, I'll give a second example of sort of the one another woman I see historically who has played a role and her name is …. And this is, this is a little bit further back then than Wangari Maathai, in the 1970s. She's, situated in the 1920s in Botswana, Southern Africa. And she developed a public health system. This was, you know, I believe, decades before the UK developed this public health system, the NHS, and she came up with the idea to use a tribal levy to sort of build up this architecture that allowed people to get access to health care. So again, when you think about sort of AI ethics more broadly as responsible innovation, people who are taking sort of an issue and, and thinking creatively and strategically about how to address that issue in a sustainable and responsible way that allows them to actually impact and get the results that they want. I think that gives a lot more room for us to bring in a lot of these African women innovators who are often not seen and in that discussion.

KERRY MCINERNEY:

That's fantastic. Yeah. And it's so wonderful to hear about those stories. And thank you for doing that. And for our listeners, we definitely encourage you to check them out. And I also really like this reframing have sort of the narrowness of ethics, which often can be boiled down to quite abstract principles, or these high level frameworks that might be different might be quite difficult to operationalize and practise into this process of responsible innovation, which to me, seems to imply forms of creativity and play, slowness, but also a real adaptability to context. And it's about the making and the process of doing technology rather than thinking very abstractly about technology.

BRIDGET BOAKYE:

Right. Right. I think that's exactly right. And you summarised it so brilliantly. Thank you, that’s precisely what I want to get across.

KERRY MCINERNEY:

No, I'm glad. And this is something I find really interesting, though, is that because you think about this as a policy specialist, but you're also you know, as you mentioned, in your introduction, you come from both a kind of arts and technical background, so you're moving between multiple spheres. And this is something Eleanor and I absolutely sympathise with, as you know, people who work very much between academia and industry. And I think the overall summary is often that absolutely no one likes you. So that's not not always the most fun. But I think it's a really important and productive space. And so, one of your main areas of expertise, as you mentioned, is the opportunities for AI adoption in Africa and the need for what you call cautious optimism in the African AI policy space. So could you explain a bit where you see opportunities for Responsible AI adoption on the African continent? And what you mean by cautious optimism and why you think this is necessary?

BRIDGET BOAKYE:

Sounds good. Yeah. Thanks for that question. So the work that we do at the Institute, and really supporting governments to understand and harness the tech revolution is grounded in needing to deliver for citizens and the people in the country. Right. And in that sense, I think AI and we believe AI is probably one of the biggest economic opportunities available to countries and their leaders at this time. So I think it's important to kind of couch that in, you know, these terms of economic opportunity, because it's very important. And we can't leave that out, that there are still countries who need those economic opportunities to sort of move up the development ladder, to give citizens an opportunity to kind of progress and live meaningful lives. And I think sometimes, as you said, depending on where you sit, it can be very myopic, and dark, or it can be sort of all hype. We sit in the middle, where there's a need for us to look at the opportunity present, but there's also a need for us to talk about how to do so responsibly. And on the responsible side, I'll come back to the bit you mentioned on cautious optimism, but I'll sort of tease out just a little bit of the opportunities we've seen in the African context. So according to the UN Economic Commission for Africa, there's an opportunity for the continent to develop a $1.5 trillion market opportunity by 2030 if they can capture 10% of the AI market. That there's some work that needs to be done in getting this there, it’s not a said and done opportunity. And what this looks like, you know, if you, if you look at the AI market in Africa, there's a lot of opportunity in three specific areas. We think about health care, and what AI can help us deliver in terms of expanding access to healthcare for millions of Africans, who at this moment do not have it. We think about the space of agriculture, and what position agriculture and all of the associated benefits from data and analytics can offer for agriculture is very important. So when we look at those spaces, you know, from healthcare, agriculture, and then to, you know, let's also look at the government service delivery. And there's a wonderful case from Togo actually where during the COVID pandemic, in collaboration with some researchers, I believe at the University of Berkeley, but I may be wrong here in terms of the school, but the story is true, but the government was able to deliver aid to places that are missing its first round of aid distribution by using satellite imagery and analysing them to kind of figure out where there was greatest need, and redeploying its resources there, after that first round of aid that was missed, and that they were able to recapture from from those analysis. So when you look at those three health care, agriculture, and then public service delivery, we see a lot of opportunities there. And then there's some opportunities we see that have already been actualized that not only present a big opportunity for Africa, but sort of, you know, will present significant benefits for the rest of the world. When you look at Africa's rich ancient history, a lot of it has been lost in the process of natural disasters, or unfortunately, sort of violence or, or, you know, sort of the host of things. And I think there's a wonderful case in Timbuktu, Mali, where Google worked with the country to develop a Mali Magic Hub that has made the timber two manuscripts that it has digitised and sort of curated all of those resources and made them widely available, not only to people in the country, but around the world. We also see that natural language processing, also, when you think about Africans, diverse language space, some 2000+ languages, and all of the incredible people I know, in Ghana and around the continent who are doing work with natural language processing, what that allows for service delivery, I mean, the if you allow the world's most linguistically diverse continent to get access to these capabilities for translation it opens up a lot of space for I think, interacting online, but also for service delivery. And it has many downstream economic benefits, so that as well. And then the last example, I'll say before I talk about cautious optimism is opportunity in wildlife and conservation. There's been developed some cloud based databases to capture and document and process wildlife and conservation efforts on the continent. So I think that's also an important space that continues to be developed. So we have an upcoming report that I know that maybe a mouthful for listeners, but we have an upcoming report that Kerry will share as well.


So in terms of cautious optimism, the way I like to think about it is we've sort of set out all of these opportunities, where, where they're being developed at this time, who are the stakeholders, etc. But I think especially in that, you know, cautious optimism should be applied globally to the AI adoption debate or discussion. But I think it's even more important in the African context where because of the enormity and complexity of some of the problems that the continent faces, it might be easy to easily sort of use a broad stroke to say AI will fix all of these many complex and, and sort of very pronounced issues. I believe AI’s benefits should not be overstated, it's a tremendous economic opportunity. But you know, sort of what it allows in terms of benefits for all people should not be overstated. There's certainly things that need to be done to ensure that its benefits are more evenly distributed. So this is where we support governments. And I think this is really the importance of our work, and helping governments to think about how to adopt AI responsibly through aligning the state of technology with the goals and their objectives to deliver for citizens, and not the hype, that we don't want to sell them on sort of, you know, AI emotion-recognition software that is not necessarily aligned with what they need to deliver for their citizens at the same time. So it's really about aligning where the state of the technology is what governments want to do to deliver for their citizens, as well as keeping in mind the resources that they have in both digital infrastructure, to enable those technologies to work efficiently in that space.


KERRY MCINERNEY:

I really like the way that you frame is trying to tread this middle ground between the kind of Silicon Valley hype, and then this kind of really dispiriting techno-pessimism. And this is actually something Eleanor and I want to do with this podcast, which was trying to think about what are all the different ways that people are approaching technology through a feminist lens? And how can this help us find a middle ground between these sort of extreme optimistic and pessimistic views? I'm really excited to A) read the report. And yes, we'll definitely have that on the website for our listeners. And then secondly, yes, to kind of follow the work that you're doing, and to talk about some of the opportunities that we have for AI development and growth. But what do you think are some of the challenges that come with AI adoption on the African continent?

BRIDGET BOAKYE:

Yeah, so there are a couple of challenges that come with responsible adoption of AI. But I'll focus on three. The first, I think you and I have talked about separately, is this idea of algorithmic colonisation, which comes from Dr. Abeba Berhane. And her work, I really appreciate in terms of mapping the idea of colonisation, or the idea of sort of someone or country establishing control over an area or country for its own use, to our sort of the use of algorithms, especially in places like Africa, she argues that some of these state of the art algorithms and AI solutions aren't suited to African problems, they are often brought in or imported from elsewhere and hinder development of local products, often leaving the continent dependent on Western software and infrastructure. She's done this work in that academic sense, but there are very real ways in which we're seeing this manifests itself, there was a recent report from MIT about surveillance software in Africa, and how a lot of the software tools that are being developed outside in terms of facial recognition software are being tested in places such as South Africa and the implications of that. So I think it's not only a conceptual or theoretical idea, but we see empirical evidence of this, and it's something that policymakers need to pay attention to. As we said, not buying into the hype or not importing solutions that don't deliver the stated outcomes, or are just plain harmful, or also don't don't give space for the development of local products and the contribution of the local ecosystem. And I think the second issue out outside would be that access and inclusion remains pivotal. Without, you know, access to just the internet or digital infrastructure a lot of the datasets on which these AI algorithms are trained will be unrepresentative of the populations that are in the populations where they're being deployed. So I think digital access and inclusion not only in getting people access to the internet, but also in getting people to actually use the internet responsibly and getting people to address some of the digital digital divide issues we see by gender, by the rural-urban divide are all still very important. We have someone at the African Woman in AI summit, someone from Access Now, who also made a very important point about sort of the growing trend of internet shutdowns and how that also affects the AI landscape through the exclusion of these, of certain people of certain countries really harms the ability for us to create meaningful datasets that are representative of populations to design and develop some of these AI products. And then the third I would say, would be the importance of legal protections, specifically, data protection and privacy, we talked about digital access and inclusion. But without data protection and privacy we sort of lose trust in the ecosystem, which hampers the development of the ecosystem more broadly. So in the African context, about half African countries as of 2020, have data protection laws. And it's important that if we're going to think about responsible adoption of AI, that we get more African countries to, you know, just at the base level, begin thinking about data protection, as an essential regulatory infrastructure that builds trust and enables people to use it enables or supports people in using the internet and knowing that they will be protected in the process and then developing developing solutions in that way.


ELEANOR DRAGE:

This episode was made possible thanks to our previous funder, Christina Gaw, and our current funder Mercator Stiftung, a private and independent foundation promoting science, education and international understanding. It was written and produced by Dr Eleanor Drage and Dr Kerry Mackereth, and edited by Laura Samulionyte.





67 views0 comments

Kommentare


bottom of page