AI, Newsrooms and African Women Journalists
5 weeks

Building and Adopting Afro‑Centric AI Tools with Cultural Localisation for African Media

5 weeks
2 students
7 lessons
By Israel Olatunji

This course empowers African women journalists, editors, and newsroom technologists to move from AI consumers to architects of culturally grounded tools. Across seven hands-on modules, participants critically audit Western‑centric bias, curate ethical local datasets, adapt LLMs for low‑resource languages, and build human‑in‑the‑loop workflows. Using no‑code platforms like Langflow, this course is designed for anyone with an aficionado interest in prototyping AI agents for real newsroom challenges—from content moderation to multilingual aggregation. The course culminates in a blueprint for digitally sovereign, African‑centric AI tools that preserve indigenous knowledge and transform how journalism serves communities.

Curriculum

  • 6 Sections
  • 7 Lessons
  • 5 Weeks
Expand all sectionsCollapse all sections
  • Module 1
    Key topics in this module include: Introduction to Generative AI in Journalism, Recognising the cultural bias in AI, Geopolitics of training data in mainstream AI - Captcha, Addressing Sexism - Stereotypes in word embeddings and Core AI Technologies - Explainable AI (XAI)
    2
  • Module 2
    Key topics in this module include: Ethical data curation, Introduction to FAIR and CARE principles and the value it provides to data and FAIRness and CAREness application for local newsrooms
    1
  • Module 3
    Key topics in this module include: Understanding and training LLM in low resourced setting using low or no-code approach, Building culturally relevant models for image recognition and translation.
    1
  • Module 4
    Key topics in this module include: Understanding the concept of Bias-Variance Tradeoff, Designing human‑in‑the‑loop workflows to ensure cultural accuracy in AI‑assisted journalism.
    1
  • Module 5
    Key topics in this module include: Understanding API? Using OSINT techniques to flag suspicious content or narratives, Toxic comment moderation and Developing training data, model card, attributes and language for comment scoring engine in locally nuanced content safety.
    1
  • Module 6
    Key topics in this module include: understanding Langflow, understanding RAG, Connecting a chat input component to a language model (LLM), Building no‑code AI agents chatbot news aggregator with Langflow, AgentQL API, and RAG and Capstone prototyping of an African‑centric AI tool with an advocacy blueprint
    1
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Israel Olatunji

Israel Olatunji Tijani a data scientist at ChatVE, an organisation building and deploying custom AI-powered solutions. With five years of hands-on data science experience, he designs the organization’s AI pipeline architecture and embeds human rights-centered, ethics-by-design principles to ensure responsible AI development. In September 2025, he presented his research abstract, “The Human Touch: Navigating the Challenges of AI Automation and Accuracy in Journalism,” at the 4S Conference in Seattle, U.S. Tijani’s contributions to AI innovation earned him a nomination in the “AI for Social Good” category at the Ghana AI Summit and Awards, hosted by The Ghana-India Kofi Annan Centre of Excellence in ICT in Accra. An award-winning professional, Tijani is a recipient of numerous continental and global fellowships. He is an alumnus of the Enterprise Development Centre at Pan Atlantic University, Lagos Business School, and the West Africa School on Internet Governance (WASIG).

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