# “Nothing about us, without us” Dumisani Ndubane reflects on ways to decolonise structured data

Intro
Welcome feminist troublemakers, friends and allies to the Whose Voices? Podcast, the audio explorations of Whose Knowledge?’s tech and knowledge justice work. Are you looking for global majority feminist perspectives on internet infrastructures, anticost and anti-capitalist approaches to tech and what a truly multilingual indigenous and queer internet could look like? Join us as we guide you through an audio journey of deconstructing power structures that underpin how we exist on and offline. So tune in, turn up the volume, and let's ignite the flames of change together because when we ask whose voices, the answer is clear: ours. In this season on Whose Voices we're in conversation with incredible activists, community builders, and change makers, providing a space to discuss how we can reimagine and redesign the internet together. This year's season is focused on decolonizing structured data with interviews carried out during Wikimania Singapore 2023 and a pre-convening we held to dive deeper into these systems. Structured data is at the core of how the internet, as we currently know it, works. These are pieces of information organized in such a way that they can be easily read, understood, and processed by machines. Through these systems, massive amounts of data get sorted out, organized and classified in relation to other pieces of data. Whose voices inform these specific regulations, traditions, and epistemologies?
Maari Maitreyi
Hello everyone and welcome again to another episode of the Whose Voices Podcast. This is Maari Maitreyi Maitreyi. I'm the knowledge justice researcher at Whose Knowledge? and I'm here today at Wikimania with Dumisani Ndubane. Dumi is an amazing wikimedian and the first convener of Wiki Indaba for African Wikimedians and Dumi also has over 10 years of experience building African Wikimedian communities. Welcome Dumi.
Dumisani Ndubane
Hey Maari Maitreyi. Glad to be here with you.
Maari Maitreyi
Same here. Dumi, do you maybe want to introduce yourself a little bit more for our listeners?
Dumisani Ndubane
Yeah, as you said, I'm Dumisani Ndubane. I'm based in Johannesburg, South Africa, and I have been a Wikimedian since 2008, a community organizer since 2011. One of the things I have enjoyed in the Wikimedia movement is one, editing on my language, Wikipedia, Xitsonga Wikipedia, which basically count almost 50% of the articles on the language have been work that I've written. In the community work I've been really passionate about representing the African agenda in the Wikimedia movement. So Wiki Indaba was one of the ways where we can ensure that African Wikimedians meet annually, where we can table issues that are peculiar to us in the region and find ways to escalate those to the larger Wikimedia movement and find allies that we can work with to get them done.
Maari Maitreyi
That is so wonderful, Dumi, and this is exactly why we have you here today. So today we're exploring questions around structured data and I was wondering if you perhaps in your own words could explain what structured data means to you. For those of our listeners who maybe are not so familiar.
Dumisani Ndubane
For me, structured data is information that has been organized in such a way that it's discoverable, searchable, and in the highest sense that a computer can easily read and make sense of it. We have always been doing this as humans. My name is structured data because when you ask me what my name is, I'll tell you my first name and surname. That's structured data. That's a way of identifying me. We do that with all other concepts in life. We use structured data to identify clothes, to identify countries, to identify concepts, and so that's my understanding of structured data.
Maari Maitreyi
I like that. Yes, we've always been structuring and classifying, categorizing and doing all these things in our different cultures. Now, when you think of the organization of structured data in places like Wikidata, for example, a database of structured data, how do you think that this kind of organizing the taxonomies, ontologies that they use fit in with African frameworks of knowledge or they don't fit in with African frameworks of knowledge?What are your thoughts?
Dumisani Ndubane
Yeah, so for me, structured data in the context of Wikidata and Africa in particular is interesting. You asked me whether it fits in or makes sense with our ways of ontology, how we describe knowledge and so on. Wikidata is very Eurocentric. We tend to describe concepts in Africa. We tend to describe interactions between people. Wikidata describes items and that's a little bit mundane for us. Like, okay, this is a chair that's a color blue, that's a person whose sex is this and that. What is it saying? For me, what I'm interested with now, which is coming up on Wikidata is lexemes, which is basically how languages get rooted, where they start, what is the meaning of that... of the word, because that for me is more interesting, like, okay, I'm Dumisani, so where does that come from? What does it mean? What other verbs or words come out of that name? That's more interesting to me. But of course for machines, this is not interesting. For machines it's good enough to identify this is a human based in this country and that's it.
Maari Maitreyi
Yes, this is true. And in addition to sort of Wikidata, what are the questions that are sort of bubbling up to the surface of your mind after day three at Wikimania, particularly around structured data?
Dumisani Ndubane (07:20):
So there's lots of questions. I was attending a session for example, where one person was identifying or at least talking about one of the identifiers in Wikidata for gender. It says gender or sex and therefore it conflates these two things. They're not the same thing, and yet that's all you have to work with in Wikidata. We know about these things for how long? And yet our technology is not catching up to these kinds of definition. So it might look like a small thing, but when you realize that Wikidata is now the biggest contributor to the large language models, what are we teaching machines about us as humans that our sex is related to our gender? That's it? So that's one of the questions, but talking about large language models itself, it was one of the interesting topics that I participated in at Wikimania.
How do these large languages get trained? They use the data that we provide or create and curate as a community, Wikidata being one of them. Now, someone said all the mistakes that we've done over the years in these projects have now been imprinted onto large language models because it has just swallowed everything that we've created in there. So we need to be careful about what we do to build structured data. The nuances we understand need to be built into the structured data that we build because if we don't, then those end up into large language models, which now seems to be the new shiny thing that everybody is attaching to. Wikidata is a database and we need to question how it has been created, what was the purpose of that creation and how it is being used. We know it is being used in large language models.
Was that the original intention for Wikidata? Maybe it was clear for some people in Europe when they were creating it, but when we participated as Africans, for me in particular, it was an easy way to get towards clearer, better info boxes for my language Wikipedia. And so I didn't think much about this gender-sex thing because I wasn't going to write about biographies. I was just writing articles. All I wanted is a simple way that this population number for this city as it sits in a central place, when it gets updated, it'll also update on my language Wikipedia. And that was the intention that I had for Wikidata. But of course we start projects without clear understanding of what the intentions are or what the potential use for the projects will be, but we cannot absolve ourselves from that. We started the project, we need to take responsibility for what the project is heading towards. And right now Wikidata has headed towards being used by large language models, which means we need to take responsibility for what's in Wikidata so that we can make sure it is still representative of the basic values that we hold dear as a community and as a movement.
Maari Maitreyi
Thank you, Dumi. I think those are such relevant points, especially about us being careful. I think about how the data we put on Wikidata is translated into models which then get used by companies and corporations, particularly interested in their own kind of business and profit making models. Now with respect to Wikimedian community as a whole, I think we talked earlier about how we are moving into this new era of AI interfacing with Wikimedia projects with Wikimedian community, but there's still a lot of folks who perhaps are not fully aware of what the policies around these interfacings or have consequences on their projects. These issues are still not so clear, and I think there's a discussion of consent to be had as well with the Wikimedian community as a whole. We were speaking a little about this earlier. Do you think you could perhaps say more?
Dumisani Ndubane
Yeah. It takes us back to the large language models that get deployed onto Wikipedia. A good example being the translation tool. There are specific technologies which the developers of course will say, no, that's not a large language model. That's machine translation, but it is a technology that's based on large language models. We need to understand which models are being used. We need to understand who created those models, what databases were used to train those models. Are they taking information from our live edits and feeding them back to the models? If so, under what kind of license? We need to understand all these nuances because when I edit Wikipedia, I'm giving consent that my work will be licensed freely on Creative Commons license. If that doesn't become the end product and it actually ends in a proprietary environment, that's a breach of contract between myself and the people who promised me that contract when I edit.
And so those questions have to be answered and I need to know and I shouldn't have to go and search through complicated UI to find out which language model am I using or contributing to and what it is built or based upon. I need to know right from the beginning this is what's available. We know for now, for example, right now that Google is one of those large language models that we are using. We don't have any alignment in terms of community values and standards with Google. So how did that relationship get created? How is Google benefiting or not benefiting from my activity on Wikipedia? The new approximation to large language models that was done on Wikipedia now, or at least machine translation as they call it, is Mint. But Mint means many other large language models or machine translation that have been bundled together. I want to know which one I'm using.
Am I using NLB, which is built by Meta or am I using the University of Helsinki? I need to know because for me that matters. I don't want to be contributing to projects that don't align with my values. And if you hide it behind the word Mint, then you're doing a disservice to me. Is this open? Is there two-way bridge between Mint and these other language models? If we don't know, I'm going to assume the worst that there is until someone tells me there isn’t. And if there is, then I'm worried about what that means for my contribution.
Maari Maitreyi
To me, you make very good points. As people who have volunteered our labor for Wikimedia and who care about the Wikimedian community, I think it's actually essential that we are given more information and there's more communication made to us. And lastly, Dumi. I was thinking what does decolonizing structured data look to you? Hm… Like… especially in the context of African languages. Broadly.
Dumisani Ndubane
I think decolonizing structured data for Africans in particular means allowing us to participate in the creation of those models, valuing community participation, incentivizing that participation so that we know that our labor is not in vain or that it's appreciated. There are different startups around the continent that are creating and building better machine translating models out there that need to find a way for them to be included onto the Wikimedia ecosystem. It is ludicrous or ridiculous for me to have to be stuck to an LLM model that gives me a 48% quality translation on the other side, which leaves me with even more work to do to clean up that translation when there are models that can give me 60, 78 to almost 80% quality output that we simply are just not including into the ecosystem. So we need a process, a clear process that says, if you have a good machine translation that works for African languages, this is how it gets included into the ecosystem. If you are interested in working with existing technologies that we have on Wikimedia, this is how you participate. So participation for us is really important. Nothing about us without us.
Maari Maitreyi
That would be an amazing ending to this episode. As always, Dumi, you've given us insights on insights. Thank you for being here. And that's the end of this episode of Whose Voices podcast.
Outro
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