Author name: Ben Cashdan

Over 2,000 African languages are spoken by approximately 1.4 billion people on the continent, showing how linguistic diversity underpins African democracy, development, and cultural life. As artificial intelligence becomes central to progress in areas like healthcare, agriculture and education, new methods for collecting and sharing African language data are urgently needed—methods that support innovation while protecting community interests. In this video we look at two pioneering initiatives in the field of ethical data sourcing in Africa. The Mining Mindset: From Extraction to Partnership Historically, many dataset projects have adopted an extractive approach: gathering language data from communities with little return or recognition for the contributors. Professor Gloria Emezue, Research Lead at Lanafrica, diagnoses this approach: “We saw the whole idea of exploitation of data as something of a mindset. When you’re out to mine something, you’re actually exploiting that thing. We now say, okay, let’s have a new approach, which we call data farming.” Rather than “mining” communities for linguistic resources, Lanafrica works to “farm” data collaboratively—turning village squares into living laboratories, involving contributors directly, and ensuring that partnerships support further community-driven research. A practical example is NaijaVoices: over 1,800 hours of curated audio data in Nigerian languages, sourced through relationships built on respect and transparency. For Lanafrica, research access is free, but commercial use involves agreements that channel support back to the language communities, creating a virtuous cycle of benefit and ongoing dataset growth. Who Owns the Data—and Who Reaps the Rewards? Behind every local dataset is a web of legal and ethical questions about ownership and benefit sharing. Dr Melissa Omino, Director at the Centre for Intellectual Property and Information Technology Law (CIPIT), Strathmore University, notes the paradox that emerges when communities contribute their voices but lose control over how their words are used: “Where do you get African languages from? Your first source would be the communities that speak the African languages, right? So you go to the community, record them, the minute you make the recording, then you have created a copyrighted work, and whoever who has made the recording owns the copyright..” According to Professor Chijoke Okorie of the Data Science Law Lab at the University of Pretoria, What ends up happening is that the communities that created these datasets end up paying for products that are built on the datasets that they created. Addressing these inequities requires new licensing models that prioritize context, impact, and fair division of benefits. NOODL Licence: Reimagining Access and Justice The Nwolite Obodo Open Data Licence (NOODL) breaks new ground in recognising African realities. It creates a two-tier licensing system: broad, cost-free access for African users, and negotiations or royalties for wealthy, commercial or international users. Communities remain central, not secondary. Professor Chijoke Okorie describes the ethos of NOODL: “Nwolite Obodo is Igbo… for community raising, community development. We’ve grown from a research group at the University of Pretoria to a research network of researchers from both Anglophone and Francophone Africa.” NOODL’s design puts community benefit at the forefront. As Dr Melissa Omino explains: “If you are from a developing country, let’s say you’re from Brazil, and you want to use the data that is licenced under this regime, then you can use it under Creative Commons licence. If you are a multi-million dollar tech company that wants to use the licence, then you need to negotiate with the AI developers who collected the data. And the licence also ensures that the community gets a benefit.” Towards More Equitable Language Data If African language data is simply extracted and exported, local voices risk exclusion—both from representation and from the benefits of digital innovation. By refocusing efforts on partnership and fair licensing, projects like Lanafrica and NOODL demonstrate that ethical, sustainable language technology is achievable. Their experiences may help guide how other communities and researchers engage with digital resources and cultural heritage—ensuring language data works for those who speak it.

A project at the Data Sciences for Social Impact (DSFSI) group, University of Pretoria, led by Professor Vukosi Marivate, is developing a talking health chatbot in African languages to provide accessible, culturally relevant health information to underserved communities. Central to the initiative is the planned use of health actuality TV and radio programmes produced by the South African Broadcasting Corporation (SABC) as training data, which introduces legal and ethical considerations around the use of publicly funded broadcast materials in AI. In the absence of South Africa’s pending copyright reforms, the project may be held up, pointing to the need for harmonised legal frameworks for AI for Good in Africa. 1. Health Inequality in South Africa and the Role of AI South Africa experiences some of the world’s highest health inequalities, shaped by historical, economic, and social factors. Urban centers are relatively well-resourced, but rural and peri-urban areas face critical shortages of health professionals and infrastructure. Language and literacy barriers further exacerbate disparities, with many unable to access health information in their mother tongue. Digital health interventions, especially those using artificial intelligence, offer a way to bridge these gaps by delivering accurate, on-demand health information in local languages. An AI-powered chatbot can empower users to make informed decisions, understand symptoms, and navigate the healthcare system, promoting greater equity in health outcomes. 2. What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a subfield of artificial intelligence that enables computers to understand, interpret, and generate human language. NLP powers applications such as chatbots, voice assistants, and automated translation tools, making it crucial for digital inclusion, especially for speakers of underrepresented languages. 3. Project Overview The DSFSI health chatbot project aims to build an AI-powered conversational agent that delivers reliable health information in multiple African languages. The project’s mission is to address health literacy gaps and promote equitable access to vital information, particularly in communities where language and resource barriers persist. 4. Data Sources and Key Resources A distinctive feature of the project is its intention to use health actuality programmes broadcast by the SABC as primary training data. These programmes offer authentic dialogues in various African languages and cover a wide range of health topics relevant to local communities. However, the use of SABC broadcast material introduces significant legal and ethical complexities. The DSFSI team has spent years negotiating with the SABC to secure permission for use of these programmes as training data, but obtaining a definitive answer has proven elusive, leaving the project in a state of legal uncertainty. 5. Legal and Ethical Challenges Copyright and LicensingSABC’s health actuality programmes are protected by copyright, with all rights typically reserved by the broadcaster. Using these materials for AI training without explicit permission may constitute copyright infringement, regardless of educational or social impact goals. Contractual RestrictionsEven if SABC content is publicly accessible, the broadcaster’s terms of use or licensing agreements may explicitly prohibit reuse, redistribution, or data mining. Absence of Research ExceptionsSouth African copyright law currently lacks robust exceptions for text and data mining (TDM) or research use, unlike the European Union’s TDM exceptions or the United States’ Fair Use doctrine. Data Privacy and Community EngagementIf the chatbot is later trained on user interactions or collects personal health information, the project must also comply with the Protection of Personal Information Act (POPIA) and ensure meaningful informed consent from all participants. 6. Public Funding and the Public Interest Argument A significant dimension in negotiations with the SABC is the broadcaster’s funding structure. The SABC operates under a government charter and receives substantial public subsidies, with direct grants and bailouts accounting for about 27% of its 2022/2023 revenue. This strengthens the argument that SABC-produced content should be accessible for public interest projects, particularly those addressing urgent challenges like health inequality and language inclusion. Many in the research and innovation community contend that publicly funded content should be available for projects benefiting the broader public, especially those focused on health literacy and digital inclusion. 7. The WIPO Broadcasting Treaty: A New Layer of Complexity The international copyright landscape is evolving, with the World Intellectual Property Organization (WIPO) currently negotiating a Broadcasting Treaty. Recent drafts propose granting broadcasters—including public entities like the SABC—new, additional exclusive rights over their broadcast content, independent of the underlying copyright. Some drafts suggest these new rights could override or negate existing copyright exceptions and limitations, including those that might otherwise permit uses for research, education, or public interest projects. If adopted in its current form, the WIPO Broadcasting Treaty could further restrict the ability of researchers and innovators to use broadcast material for AI training, even when the content is publicly funded or serves a vital social function. 8. The Copyright Amendment Bill: Introducing Fair Use in South Africa A potentially transformative development is the Copyright Amendment Bill, which aims to introduce a Fair Use doctrine into South African law. Modeled after the U.S. system, Fair Use would allow limited use of copyrighted material without permission for research, teaching, and public interest innovation—the core activities of the DSFSI health chatbot initiative. If enacted, the Bill would provide a much-needed legal pathway for researchers to use materials like SABC broadcasts for AI training, provided the use is fair, non-commercial, and does not undermine the market for the original work. However, the Bill has faced significant opposition and delays, and is currently under review by the Constitutional Court, leaving its future uncertain. 9. Contractual or Policy Barriers In the absence of clear research exceptions, the project team must review and potentially negotiate with the SABC to secure permissions or licenses for the intended use of broadcast content. Without such agreements, the project may be forced to exclude valuable data sources or pivot to community-generated content. 10. Cross-Border and Multi-Jurisdictional Issues If the chatbot expands to use or serve content from other African countries, it will encounter a patchwork of copyright and data protection laws, further complicating compliance and cross-border collaboration. 11. Conclusions The challenges faced by the DSFSI health chatbot project

The Masakhane Project showcases the transformative power of open, collaborative efforts in advancing natural language processing (NLP) for African languages. However, its reliance on the JW300 dataset—a vast multilingual corpus primarily comprising copyrighted biblical translations—uncovered significant legal and ethical challenges. These challenges focused on copyright restrictions, contract overrides, and the complexities of cross-border data use. This led to the discontinuation of JW300’s use within Masakhane, prompting a shift toward community-generated data. The experience illustrates the urgent need for robust copyright exceptions, clear legal frameworks, and ethical data sourcing to foster innovation and inclusivity in global NLP research. A video version of the case study is available below. 1. What Is Natural Language Processing? Natural Language Processing (NLP) is a branch of computer science and artificial intelligence focused on enabling computers to understand, interpret, and generate human language, both written and spoken. NLP integrates computational linguistics with machine learning, deep learning, and statistical modeling, allowing machines to recognize patterns, extract meaning, and respond to natural language inputs in ways that approximate human comprehension. NLP underpins many everyday technologies, including search engines, digital assistants, chatbots, voice-operated GPS systems, and automated translation services. NLP is crucial for breaking down language barriers and has become integral to the digital transformation of societies worldwide1. 2. Masakhane Project Overview The Masakhane Project is an open-source initiative dedicated to advancing NLP for African languages. Its mission is to democratize access to NLP tools by building a continent-wide research community and developing datasets and benchmarks tailored to Africa’s linguistic diversity. By engaging researchers, linguists, and technologists across the continent, Masakhane ensures that African languages are not marginalized in the digital age. The project employs advanced sequence-to-sequence models, training them on parallel corpora to enable machine translation and other NLP tasks between African languages. The distributed network of contributors allows Masakhane to address the unique challenges of Africa’s linguistic landscape, where many languages lack sufficient digital resources. A notable achievement is the “Decolonise Science” project, which creates multilingual parallel corpora of African research by translating scientific papers from platforms like AfricArxiv into various African languages. This initiative enhances access to academic knowledge and promotes the use of African languages in scientific discourse, exemplifying Masakhane’s commitment to African-centric knowledge production and community benefit. 3. JW300 Dataset and Its Role The JW300 dataset was pivotal to Masakhane’s early work. It offers around 100,000 parallel sentences for each of over 300 African languages, mostly sourced from Jehovah’s Witnesses’ biblical translations. For many languages, JW300 is one of the only large-scale, aligned text sources available, making it invaluable for training baseline translation models such as English-to-Zulu or English-to-Yoruba. Masakhane utilized automated scripts for downloading and preprocessing JW300, including byte-pair encoding (BPE) to optimize model performance. Community contributions further expanded the dataset’s coverage, filling language gaps and improving resource quality. JW300’s widespread use enabled rapid progress in building machine translation models for underrepresented African languages. 4. Copyright Infringement Discovery Despite JW300’s open availability on platforms like OPUS, its use was legally problematic. In 2023, a legal audit by the Centre for Intellectual Property and Information Technology (CIPIT) in Nairobi revealed that the Jehovah’s Witnesses’ website explicitly prohibited text and data mining in its copyright notice. This meant Masakhane’s use of JW300 was unauthorized. When Masakhane’s organizers formally requested permission to use the data, their request was denied. This highlighted a fundamental tension between Masakhane’s open research ethos and the proprietary restrictions imposed by the dataset’s owners, forcing the project to reconsider its data strategy. 5. Copyright Exceptions and Limitations: The Role of TDM Exceptions and Fair Use Many jurisdictions provide copyright exceptions and limitations to balance creators’ rights with the needs of researchers and innovators. The European Union’s text and data mining (TDM) exceptions and the United States’ Fair Use doctrine are prominent examples. The EU’s Directive on Copyright in the Digital Single Market (Directive (EU) 2019/790) introduced two mandatory TDM exceptions. The first allows research organizations and cultural heritage institutions to conduct TDM for scientific research, regardless of contractual provisions. The second permits anyone to perform TDM for any purpose, provided the rights holder has not expressly opted out. Recent German case law clarified that an explicit reservation in a website’s terms is sufficient to exclude commercial TDM, but the exception remains robust for research contexts. In the U.S., the Fair Use doctrine allows limited use of copyrighted material without permission for purposes like criticism, comment, teaching, scholarship, or research. Courts increasingly recognize that using copyrighted works to train AI models can qualify as Fair Use, especially when the use is transformative and does not harm the original work’s market. Had Masakhane operated in the EU or U.S., these exceptions might have provided a legal basis for using JW300 for non-commercial research. However, most African countries lack clear TDM provisions or Fair Use recognition, exposing researchers to greater legal uncertainty and risk. The Masakhane experience underscores the need for African nations to adopt or clarify copyright exceptions that support research and digital innovation. 6. Contract Overrides Contract overrides occur when contractual terms—such as website terms of service—impose restrictions beyond those set by statutory copyright law. In JW300’s case, Jehovah’s Witnesses’ website terms explicitly prohibit text and data mining, overriding any potential exceptions or fair use provisions. For Masakhane, this meant that even if their use could be justified under fair use or research exceptions in some jurisdictions, the contractual terms imposed stricter limitations. Only in jurisdictions where statutes prevent contracts from overriding copyright exceptions (such as the EU’s TDM provision for research institutions) could these terms be challenged. This highlights the importance of reviewing all terms of service and data use agreements before using third-party datasets, especially in open, cross-border research projects. 7. Cross-Border Use The cross-border nature of datasets like JW300 adds further legal complexity, especially for open research projects with contributors across multiple countries. Masakhane operates in a pan-African context, with team members and users in different nations. Copyright and data use laws vary widely. What is permissible under fair